Industrial Visual Inspection: The Allure of Multimodal Large Models
2026-06-26
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I. A Tantalizing Question
Shortly after the launch of GPT-4V in early 2023, we received a call from a long-term client.
He served as the technical director of a home appliance manufacturer. Two years prior, we had deployed a surface inspection system based on YOLOv5 for their factory, which had been operating stably ever since.
He raised a thought-provoking question over the phone:
“I’ve seen that GPT-4V can interpret all kinds of images and recognize nearly everything. Can we adopt it directly for quality inspection? Would that eliminate the need for data labeling entirely?"
I held back a straightforward answer back then.
Truth be told, we were equally captivated by the idea ourselves.
Demos of multimodal large models are undeniably impressive. Feed the model any random image, and it can outline contents, pinpoint defects and classify fault types. No training or labeling is required; it delivers zero-shot performance out of the box.
If this capability translated seamlessly to factories, the entire rulebook for industrial visual inspection would be rewritten.
We spent nearly two years testing diverse multimodal large model solutions across multiple projects.
Our conclusion is clear: tempting as the technology may seem, real-world industrial application comes with harsh limitations.
This article documents all the pitfalls we encountered over these two years.
II. Establish the Current Landscape: YOLO Has Become the De Facto Standard
Before diving into multimodal large models, it is critical to lay out the industry baseline:
The dominant solution for today’s industrial visual inspection relies on object detection and segmentation models represented by the YOLO series.
This is hardly a new trend. Starting from YOLOv3, through the widely deployed YOLOv8, YOLOv9 and YOLOv10, the YOLO family has been implemented in industrial production lines for years, boasting a fully mature technical stack.
Why Has YOLO Become the De Facto Standard?
First, ultra-fast inference speed.
Equipped on standard edge computing boxes paired with industrial cameras, YOLOv8 completes inference for one frame within 10 to 30 milliseconds, matching the takt time of most production lines.
Second, sufficient detection accuracy.
With adequate labeled datasets, the YOLO series achieves outstanding precision for common defect categories, easily hitting an mAP of over 90%.
Third, mature deployment ecosystem.
Ready-made toolchains support multiple deployment frameworks including ONNX, TensorRT and OpenVINO. The full workflow from model training to on-site deployment has been validated by countless industrial projects.
Fourth, comprehensive open-source ecosystem.
The active open-source community provides accessible fixes for most technical hurdles, with abundant pre-trained weights, data augmentation kits and labeling tools readily available.
Therefore, the YOLO series is practically the default choice for industrial visual inspection projects launched in 2024.
There is no need to debate whether deep learning should be adopted — that question was settled a decade ago.
The new core question now arises: With the emergence of multimodal large models, does YOLO still remain the optimal solution?
III. The Allure of Multimodal Large Models: A Promising Mirage
2023 witnessed an explosive wave of multimodal large model releases.
Models including GPT-4V, Gemini and Claude 3 deliver powerful general image comprehension capabilities.
We have run tests on these models, and honestly, their demo performances are truly impressive:
Allure 1: Zero-Shot Capability
Traditional workflow: To inspect a specific type of defect, you first need to collect, label and train on images of that defect. No data means no usable model.
Multimodal large models: Simply describe your demand in natural language, such as “Check whether there are scratches in this image", and the model will return results instantly. No training or labeling required.
What does this mean? The cold-start cost drops close to zero.
When launching new products, there is no need to spend two weeks on data collection, labeling and model training. You can put the model into use merely with a few lines of prompts.
Allure 2: Advanced Semantic Comprehension
Traditional models only output bounding boxes and confidence scores, e.g. “A defect exists within this box with a confidence of 0.87".
Multimodal large models generate descriptive natural language: “A scratch of around 2cm appears at the top-left corner of the picture, likely formed during transportation. It is recommended to optimize the packaging process."
What does this mean? Inspection results can be directly converted into formal quality inspection reports.
Allure 3: Powerful Generalization Capacity
Traditional models can only recognize defect types seen during training; they fail to identify brand-new unseen defects.
In theory, multimodal large models have processed massive images sourced from the internet, enabling them to potentially recognize all kinds of rare and irregular defects.
What does this mean? Coverage for long-tail defects and abnormal edge cases is drastically improved.
Allure 4: Interactive Inspection Logic
Traditional solutions embed fixed inspection rules into the model. Revising inspection criteria requires full retraining.
Multimodal large models support dynamic adjustment of standards via prompts. For instance, you can set the threshold as “scratches over 1cm count as NG" one day and switch it to “0.5cm" the next without modifying the underlying model.
What does this mean? Tuning inspection standards becomes extremely flexible.
Reading all these advantages, you may also be tempted — just as we were back then. That’s why we decided to deploy multimodal large models in several real projects, only to run into a string of costly pitfalls afterward.
IV. Six Costly Pitfalls Encountered in Practical Deployment
Pitfall 1: Excessive Inference Latency Unsuitable for Production Lines
Our pilot project focused on appearance inspection for mobile phone housings.
The production line processes one workpiece every 3 seconds, meaning total inspection latency must stay below 2 seconds to reserve 1 second for robotic sorting.
We tested the GPT-4V API workflow:
Upload the image and input the prompt
Wait for server response
Receive inspection results
Average latency hit 4–6 seconds, and could exceed 10 seconds amid network fluctuations — far too slow for the assembly line.
You might suggest self-hosted open-source multimodal models such as LLaVA and Qwen-VL instead. We tested these as well. Running LLaVA-13B on an A100 GPU yields single-image inference latency of roughly 800ms to 1.2 seconds.
While faster than cloud APIs, it remains dozens of times slower than YOLO.
Pitfall 2: Skyrocketing Throughput and Computing Costs
Even if we tolerate the latency for argument’s sake, the cost calculation tells a harsh story.
How many images does one production line process daily?
Assuming one workpiece every 3 seconds and 20 hours of daily operation, a single line generates around 24,000 inspection images per day.
For GPT-4V API, unit pricing ranged from $0.01 to $0.03 per image, depending on resolution and token consumption:
Daily cost per line: $240–$720
Monthly cost per line: $7,200–$21,600
Annual cost per line: $86,400–$259,200
This only accounts for one line, while our client operated 12 production lines — an unaffordable expense for manufacturers.
What about self-hosted open-source models?
A single A100 GPU delivers roughly 1–2 QPS (queries per second). A single line peaks at around 0.3 QPS, seemingly manageable with one card for multiple lines.
However, factoring in servers, IDC space and maintenance, the annual operating cost for an A100 deployment runs into hundreds of thousands of RMB.
In contrast, a YOLO deployment only requires an edge computing box costing a few thousand RMB to support one full production line.
The cost gap spans two orders of magnitude.
Pitfall 3: Unstable, Probabilistic Outputs — Inconsistent Results for Identical Images
This proved our most frustrating roadblock.
Industrial inspection demands absolute determinism: identical images must yield identical inspection results every single time, otherwise standardized quality control and traceability become impossible.
Multimodal large models, however, produce probabilistic outputs.
We ran a controlled test: feeding the same defective image with an identical prompt to GPT-4V ten separate times. The outcomes varied drastically:
7 runs labeled the product defective
2 runs marked it suspected defective requiring manual review
1 run claimed no obvious defects existed
All from the exact same input and prompt.
Such randomness is fatal for factory quality control. Inspectors cannot act on a “70% chance of defect" output — every workpiece needs a definitive OK or NG verdict.
Some propose setting temperature to 0 for consistency. We tried this method, which improved stability yet failed to guarantee 100% identical outputs. Large models generate results via sampling mechanisms, and minor deviations persist for edge cases even with temperature = 0.
Pitfall 4: Fragile Prompt Engineering — Minor Wording Shifts Alter Judgments
Multimodal model performance hinges entirely on prompt design, which we spent extensive manpower optimizing to boost accuracy and stability.
We soon discovered prompts are extremely sensitive to wording changes.
Three prompts with nearly identical core requests delivered vastly different inspection outcomes:
Prompt A: “Check whether surface defects exist in this image."
Prompt B: “Carefully examine the product surface and identify scratches, pits, foreign matter and other defects."
Prompt C: “Act as a professional quality inspector. Locate and classify any appearance defects on the product in this image."
Worse still, prompts fine-tuned for Product A lose efficacy when applied to Product B, requiring full rework of prompt logic for every new product variant.
How does this differ from retraining YOLO models for new products?
YOLO training relies on quantifiable evaluation metrics to clearly signal when the model meets standards; prompt tuning depends entirely on subjective trial and error, with no clear benchmark for optimal performance.
Pitfall 5: Hallucination — Fabricating Non-Existent Defects with Confidence
Hallucination is a well-documented flaw of large language and multimodal models: the system confidently invents details that do not exist.
In industrial inspection, this manifests as three typical failures:
Flagging defect-free products as defective
Misstating defect positions (e.g. locating scratches on the left when they appear on the right)
Misclassifying defect types (e.g. labeling pits as scratches)
One test case exemplifies the severity: an entirely flawless product image triggered a highly detailed fabricated analysis: “A shallow scratch approximately 3mm long is detected at the bottom-right corner, functional impact assessment recommended."
Upon close visual review, no mark or scratch was present in that region at all.
If such hallucinations infiltrate mass production lines, severe consequences follow: either defective goods slip through undetected (missed inspection) or qualified products get wrongly rejected (false rejection).
Pitfall 6: High Resource Barriers for Private On-Premise Deployment
As cloud APIs suffer high latency and excessive cost, self-hosted deployment seems like an alternative. We evaluated hardware and software requirements for mainstream open-source multimodal models:
How About YOLO?
YOLOv8-m runs smoothly even on a GTX 1080 with 8GB VRAM.
It can even be deployed on edge computing hardware such as NVIDIA Jetson modules with power consumption of merely tens of watts.
The computational resource threshold differs by an entire order of magnitude.
For most factories, installing an A100 server on the production floor is impractical in terms of both capital expenditure and daily operation & maintenance.
V. Back to First Principles: What Exactly Does Industrial Visual Inspection Require?
After stumbling through all the above pitfalls, we stepped back to reflect on a fundamental question:
What core capabilities are essentially demanded by industrial visual inspection?
Deterministic Output
Identical images must yield 100% consistent results. This forms the foundation of standardized quality control and full traceability; probabilistic outputs are unacceptable.
Ultra-Low Latency
Millisecond-level response. Production line takt time is rigid, and inspection cannot become a bottleneck.
A 10ms inference time and a 1,000ms inference time represent entirely different operational realities.
High Throughput
How many frames can be processed per second? How many workpieces can be inspected daily?
Computational costs must remain controllable, avoiding annual expenses of hundreds of thousands of US dollars for a single production line.
Edge Deployment Compatibility
Factory network environments are complex; many workshops lack stable or accessible internet connections.
Models must operate locally on edge devices rather than relying on cloud APIs.
Interpretable Inspection Results
When a defect is detected, the system needs to clearly inform inspectors of its exact location and category.
Ideally, it should output defect coordinates, area and confidence scores for downstream system integration.
Controllable Maintenance Costs
Products get upgraded and inspection standards are revised on a regular basis.
The adaptation cost for every iteration must be manageable, without full reconstruction each time.
Matching these six core requirements against the two technical routes reveals a clear contrast:
YOLO Series meets all six criteria perfectly
Determinism: 100% consistent outputs given identical input
Low latency: 10–30 millisecond inference
High throughput: Dozens to over a hundred QPS per single GPU
Edge-deployable: Fully compatible with Jetson hardware and industrial PCs
Interpretable outputs: Bounding boxes, defect categories and confidence values
Low maintenance overhead: Mature toolchains for incremental training and transfer learning
Multimodal Large Models fail nearly every requirement
Determinism: Inherently probabilistic output
Latency constraint: Second-scale inference
Throughput limit: Single GPU only supports single-digit QPS
Edge deployment barrier: Demands A100-class high-end GPUs
Interpretability gap: Raw natural language descriptions require secondary parsing
Unpredictable maintenance: Prompt engineering lacks quantifiable optimization standards
So can multimodal large models replace YOLO? The conclusion is unambiguous:
At the current stage of technical maturity, multimodal large models are unsuitable as the primary solution for industrial visual inspection.
Its strengths including zero-shot reasoning, deep semantic comprehension and strong generalization deliver little practical value on production lines; meanwhile its critical flaws — high latency, prohibitive costs and unstable outputs — are catastrophic for industrial quality control.
VI. Not Replacement, But Complementation
This does not mean multimodal large models are completely useless for industrial visual inspection.
The key lies in identifying their proper niche.
After two years of field trials, we have summarized four scenarios where multimodal large models create tangible value:
Scenario 1: Auxiliary Automated Data Annotation
Annotation constitutes the biggest cost driver of traditional inspection projects.
An industrial vision task usually requires thousands to tens of thousands of annotated images. Outsourcing annotation services costs several tenths to several US dollars per frame, with labeling expenses accounting for 30%–50% of total project investment.
Multimodal large models deliver pre-labeling capability:
The model generates preliminary annotation masks and boxes from raw images first. Human staff only need to review and revise results instead of labeling from scratch.
Our field tests prove this workflow boosts annotation efficiency by 3–5 times, cutting average labeling time per image from 30 seconds to under 10 seconds.
Scenario 2: Fallback Coverage for Long-Tail Defects
The performance ceiling of YOLO models is straightforward: they can only recognize defect types featured in training datasets.
Unprecedented rare defects will trigger missed detection by YOLO.
Although such long-tail anomalies occur infrequently, they often signal severe abnormal manufacturing conditions, carrying higher operational risks.
Multimodal large models act as a fallback verification layer:
When YOLO outputs a borderline confidence score (roughly 0.3–0.7, the gray zone of uncertainty), the corresponding image is sent to the multimodal model for secondary judgment.
The zero-shot generalization strength of large models covers these unseen rare anomalies.
Under this mechanism, only 5%–10% of all images are forwarded to the multimodal model, keeping total costs manageable while drastically improving coverage of long-tail defects.
Scenario 3: Semantic Conversion of Raw Inspection Data
YOLO only outputs structured data: bounding boxes, defect categories and confidence scores.
While sufficient for backend industrial systems, these raw metrics are unintuitive for human inspectors, who need answers to practical questions: How severe is the defect? What caused it? What corrective action should be taken?
Multimodal large models perform semantic report generation:
Input: Defect coordinates, classification labels, product model and manufacturing process parameters
Output: Natural language inspection report, e.g. “A 5mm scratch is detected on the left edge of the product, likely caused by mold abrasion; mold maintenance is recommended."
This task is latency-insensitive (reports can be generated asynchronously) and cost-efficient (only executed on NG non-conforming products with limited volume).
Scenario 4: Rapid Cold Start for Small-Sample Urgent Projects
Clients occasionally face tight deadlines: new products scheduled for mass production the following week with merely dozens of defective sample images, insufficient for full YOLO training.
Traditional workflow cannot launch inspection under such limited data.
Multimodal large models serve as a transitional temporary solution:
Zero-shot capability enables immediate deployment with acceptable yet imperfect accuracy, far outperforming full manual inspection. Data can be continuously collected during pilot operation to train a formal YOLO model for long-term use once sufficient samples are accumulated.
VII. Hybrid Architecture: Our Practical Deployment Paradigm
Based on the above analysis, we have adopted a hybrid dual-channel architecture for recent industrial projects:
Main Inspection Channel: YOLO
Handles over 95% of all inspection workloads
Deployed locally on edge hardware with 10–20ms inference latency
Outputs structured bounding boxes, defect types and confidence scores
Auxiliary Channel: Multimodal Large Model
Only processes borderline low-confidence images within the gray zone
Invoked asynchronously without disrupting main line throughput
Functions for long-tail defect fallback verification, semantic report generation and auxiliary labeling
Core design principles of this hybrid framework:
YOLO acts as the core primary system; multimodal models serve as auxiliary tools — avoid reversing their roles
Data shunting instead of serial processing: multimodal models stay off the critical production path and impose no impact on main-line latency or throughput
Confidence-based traffic splitting: high-confidence results pass through directly, while ambiguous samples are forwarded for secondary multimodal validation
Predictable cost control: only a small fraction of images consumes multimodal model computing resources
VIII. Technical Selection Decision Framework
Below is a summarized decision tree for teams selecting industrial visual inspection algorithms:
Latency Requirement
Required inference
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Hikvision industrial cameras are facing widespread stock shortages, and the truth is far more complex than mere "stockpi
2026-06-18
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Full English Translation (Industry In-depth Article Tone)
Industry Pains and Transformations Amid the Restructuring of Full-Chain Strategy
For practitioners engaged in machine vision and equipment integration, one common headache has lingered since last year: Hikrobot industrial cameras have grown increasingly hard to source.
From the industry’s most widely deployed measuring models — 2/3-inch 5MP, 1-inch 20MP C-mount variants — to standard area-scan cameras, spot inventories across distribution channels remain chronically tight, with lead times repeatedly extended. This has spurred widespread speculation across the trade: Is Hikrobot deliberately limiting output to drive price hikes? Or leveraging its market dominance to crowd out competitors?
English Translation (Industry analysis formal tone)
However, stepping back from the immediate spot supply shortage and analyzing from the perspective of corporate strategy and industry cycles reveals that the current stock shortage is by no means a simple market manipulation. Instead, it is an inevitable outcome of Hikrobot’s top-down comprehensive strategic adjustments covering product lines, production capacity, distribution channels and business priorities. Restraints in the upstream supply chain and surging downstream demand have only exacerbated the severity of supply shortages.
In short: strategic layout adjustment is the fundamental root cause, market consolidation a collateral outcome, and supply-demand mismatch a short-term aggravating factor.
一. Core Fundamental Logic: Supply Shortages Root in Full Industrial Chain Strategic Restructuring
Many people equate the supply shortage with "capping output to drive up prices", but they have confused cause and effect. Hikrobot’s core strategic move is to complete a comprehensive upgrade and restructuring of all business lines during the transition window of product renewal and capacity relocation. The supply shortage is only a temporary growing pain arising from this transition.
1. Product Line Iteration: Full Migration to CU Platform, Phase-out of Legacy CS/CH Series
Starting from the second half of 2025, Hikrobot has issued multiple Product Change Notices (PCNs), gradually discontinuing its high-volume legacy CS and CH industrial camera series, and fully shifting to the new-generation cost-effective CU models and premium AI-CH cameras.
Supply chain perspective: No additional orders will be placed for legacy CMOS and FPGA chips. Formal production suspension takes effect once existing raw materials are depleted, and distributors will no longer receive restock allocations for discontinued models.
Market perspective: The widely adopted 2/3-inch 5-megapixel C-mount global shutter cameras — the primary models compatible with domestic telecentric lenses — have borne the brunt, resulting in widespread supply outages.
Underlying strategic goal: Standardize the hardware R&D platform to streamline production lines and cut material management costs. In addition, the new platform embeds ISP and lightweight AI preprocessing functions to precisely meet emerging high-end inspection demands from lithium battery, photovoltaic, 3C electronics and other manufacturing sectors.
二. Restructuring of Capacity Layout: Ramp-up of Tonglu New Base Creates Supply Gap During Transition Between Old and New Production Lines
The mismatch between dwindling old capacity and yet-to-mature new production lines constitutes the most direct supply-side cause of product shortages.
Hikrobot’s Tonglu Intelligent Manufacturing Base, with a total investment of 1.534 billion RMB, is designed for an annual output of 5 million machine vision products and only entered full-scale production in early 2026. Meanwhile, the old factories have gradually cut production and begun equipment relocation. During the overlapping operation period of old and new production lines, production capacity was split between two sites, making it impossible to fulfill the massive order volume as before.
Coupled with the explosive concentrated demand for inspection equipment in lithium battery, photovoltaic and semiconductor industries over the past two years, the manufacturer can only allocate available stock by project priority. Top key customers receive priority supply, leaving small and medium-sized equipment integrators and scattered retail orders struggling to secure cameras.
三. Shift of Business Focus: Resource Reallocation Toward 3D Vision and Full-Stack Solutions
In recent years, Hikrobot’s strategic priority has shifted far beyond standalone hardware sales to full-stack embodied intelligent manufacturing solutions. Integrated systems combining vision, AGVs and mobile robots represent its core growth driver for the future.
At the supply chain level, procurement quotas for upstream CMOS sensors and storage chips are prioritized for high-margin, high-value-added products including 3D cameras, smart code readers and vision controllers, while chip allocations for traditional 2D area-scan cameras are intentionally reduced.
Compounding the strain, global wafer fabs are diverting most capacity to AI computing chips and HBM memory, resulting in a more than 30% capacity contraction for industrial-grade global shutter CMOS and FPGAs. The dual pressure of internal resource reallocation and external component shortages has drastically widened supply gaps for 2D cameras.
4. Revamp of Distribution System: Cut Bulk Spot Allocations, Secure Long-Term Orders via Direct Contracts with Top Clients
Tightened distribution policies are the most visible trigger for widespread stock shortages among end users.
Since late 2025, Hikrobot has rolled out stricter channel rules, slashing spot inventory quotas for small and medium-sized distributors. Instead, it prioritizes signing annual framework agreements with leading equipment manufacturers in lithium battery and photovoltaic sectors, locking up large volumes of spot stock under long-term contracts in advance.
This has created a clear industry divide: large manufacturers enjoy stable order fulfillment with guaranteed supply, while small and medium integrators and small-batch urgent retail orders face a severe lack of available stock, amplifying the perception of shortages across distribution channels.
II. Objective Outcome: Accelerated Industrial Consolidation, Not a Deliberate Target
It is critical to clarify that the current supply shortage was not engineered by Hikrobot to deliberately cut output, suppress competitors or monopolize the market. Industrial reshuffling and market restructuring are merely secondary side effects arising from its strategic overhaul.
Replacement Opportunities for Second-Tier Domestic Brands
Massive numbers of small and medium equipment integrators have been forced to adopt domestic alternative solutions, driving a sharp surge in orders for brands including Huaray, Daheng, ECOVIS and MindVision, alongside rapid growth in their market share.
Phase-Out of Low-End Low-Margin Capacity
Hikrobot’s voluntary discontinuation of low-margin legacy camera models is depleting low-price stock in the market, lifting the overall average product price and weeding out small vision manufacturers that rely solely on price competition without proprietary solution capabilities.
Widened Competitive Edge for Industry Leaders
For Hikrobot, hardware shortages barely impact delivery of its integrated solution orders. Long-term partnerships anchored by full-set solutions solidify its core major customer base, widening the gap with small manufacturers that only supply standalone cameras.
In short: consolidation is a consequence, not an original objective. This is not a premeditated market crackdown, but a natural industrial reshuffle brought about by corporate upgrading.
III. Three Overlapping Factors Exacerbating Supply Shortages
While strategic restructuring constitutes the fundamental root of stock shortages, the triple convergence of upstream constraints, downstream demand spikes and product transition cycles has pushed supply gaps to a level felt throughout the entire industry.
Hard Constraints from Upstream Supply Chains
Global semiconductor foundries prioritize capacity for high-end computing chips, leaving industrial CMOS and FPGAs hardest hit: production capacity for related components has shrunk by over 30%, with lead times extended from the original 4 weeks to more than 12 weeks. Even running production lines at full tilt, manufacturers face a critical shortage of core components.
Meanwhile, rising prices of raw materials such as copper and PCBs discourage manufacturers from excessive stockpiling due to cash flow and inventory risk concerns, further limiting supply flexibility.
Concentrated Surge in Downstream Demand
2026 marks a peak year for mass production of new energy and semiconductor inspection equipment. Mass rollout of projects including lithium electrode inspection, photovoltaic silicon wafer sorting and semiconductor appearance inspection has driven a year-on-year surge of over 65% in demand for high-precision measuring cameras, far outpacing the release speed of existing production capacity.
Supply Disruption During Transition Between Old and New Product Lines
Full discontinuation of legacy models coincides with low mass-production yield rates for the new CU series, creating a natural 3–6 month supply vacuum. Limited initial production capacity of the CU platform is allocated first to major key customers, further squeezing spot inventory available through open distribution channels.
IV. Three Profound Long-Term Impacts of Industry-Wide Supply Tightness
Widespread stock shortages send ripple effects across every participant in the industrial chain, bringing short-term growing pains alongside lasting structural shifts.
1. For Equipment Integrators: Short-Term Disruption, Long-Term Resilient Supply Chains
Short-term impact: Project delivery timelines are delayed due to depleted mainstream C-mount measuring camera stock. Many integrators are forced to switch to alternative brands temporarily, incurring extra costs for prototype testing and program adaptation.
Long-term benefit: Companies are compelled to build multi-brand alternative product libraries, reducing reliance on a single supplier and boosting overall supply chain risk resistance.
2. For the Competitive Landscape: Stratified Domestic Market, Benefits for Supporting Industries
A two-leader domestic market pattern is taking shape: Hikrobot dominates the high-end integrated solution segment, while Huaray absorbs substitution demand with steady stock to capture mainstream market share. Brands such as Daheng and MindVision rapidly seize market space previously held by small integrators.
Imported brands see mild short-term demand recovery: players including Basler and Cognex have secured partial high-end replacement orders, yet lead times exceeding 8 weeks restrict their application to only premium precision inspection scenarios.
3. For Hikrobot Itself: Short-Term Loss of Retail Clients, Improved Long-Term Corporate Value
Short-term downsides: A large volume of small-batch retail orders is lost to competitors, with some projects poached by rival manufacturers; distributors face mounting inventory pressure and growing dissatisfaction.
Long-term upsides: Low-margin product lines are phased out, shifting the product portfolio toward high-value 3D vision and AI inspection solutions. Once the Tonglu manufacturing base reaches full capacity, overall output will double for drastically improved long-term supply stability. Direct long-term contracts with major clients also lock in revenue streams for years to come.
五. When Will Shortages Ease? Practical Solutions Available Right Now
This is the top concern for all industry practitioners. We provide forecasts based on production capacity and product cycles, along with implementable solutions for mainstream application scenarios.
1. Forecasted Timeline for Supply Recovery
Based on current progress, the Tonglu Intelligent Manufacturing Base is expected to reach full capacity by the end of 2026. Coupled with steady mass production yields of the CU series and newly launched upstream CMOS wafer production capacity, supply of standard 2D area-scan cameras is projected to return to normal in Q1 2027.
It is important to note that legacy CS and CH series have been permanently discontinued with no plans for resumption of production. Future system design must fully adopt the new platform or alternative brands.
2. Readily Applicable Camera Selection Strategies
Two categories of recommendations are provided for the most widely deployed camera applications across industries:
Emergency Replacement Solution
Brands including Huaray and Daheng offer products with fully matching parameters equivalent to discontinued Hikrobot legacy models, supported by ample spot inventory. Minimal software modification is required to enable fast migration.
Long-Term Project Solution
Enterprises planning new projects may place advance orders to reserve stock of Hikrobot’s new CU series cameras.
Closing Remarks
Looking back at the development of China’s machine vision industry, every iteration of production capacity and product lineup is accompanied by cyclical supply and demand fluctuations.
The ongoing supply shortage of Hikrobot cameras is essentially an inevitable transition for a market leader upgrading from a pure hardware manufacturer to a full-stack solution provider. Phasing out outdated production capacity, migrating to new hardware platforms, and restructuring distribution channels and business priorities all come with transitional growing pains. Cyclical volatility in upstream semiconductor supply chains and the explosive demand from the new energy sector have amplified the industry-wide impact of this transformation.
For all players in the sector, rather than dwelling on debates over intentional price manipulation, it is more prudent to establish multi-brand camera libraries and diversified supply chain backups to maintain stable operations amid industry shifts.
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Zero in on core processes, deliver one-stop empowerment for PV & energy storage intelligent manufacturing upgrades! Visi
2026-06-04
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SNEC 2026
Held from June 3 to 5, SNEC 2026 Shanghai International Photovoltaic Exhibition grandly opens at the National Exhibition and Convention Center (Shanghai). Hikrobot makes a prominent appearance with intelligent solutions covering the full photovoltaic production process chain.
Spanning silicon wafer slicing, solar cell fabrication, module encapsulation to high-precision inspection, Hikrobot underpins product quality with fully self-developed core technologies, exploring new pathways for PV-storage integration and smart manufacturing upgrading alongside numerous on-site industry visitors.
Micron-level sensing for slicing processes enables rigorous silicon wafer quality control.
01 Wafer Thickness Inspection
This solution adopts six 3D profile sensors to measure wafer thickness. Deployed in opposite paired layout, the system simultaneously acquires three groups of measurement data and is applicable to silicon wafer sorting stations. Equipped with built-in algorithms against ambient light interference, specular reflection interference and vibration suppression, the cameras deliver greatly improved measurement accuracy and operational stability.
3D Vision Empowers Module Production for High-efficiency Flexible Manufacturing
01 PV Junction Box 3D Visualization
Driven by high-speed oscillation of the galvanometer inside the 3D camera, the solution rapidly sweeps laser lines across target surfaces to capture complete 3D topography of junction boxes in a single scan. Even for messy piled black wiring harnesses on junction boxes, the camera generates refined and intact 3D point clouds to realize rapid and precise identification, facilitating high-efficiency flexible production in PV module assembly procedures.
AI Empowers Solar Cell Production to Maximize Inspection Performance
01 Microcrack Inspection
The solution adopts 4K monochrome line scan cameras paired with large-format short-wave infrared lenses and transmission-type near-infrared laser light sources to detect and classify defects including crystal detachment, edge chipping, fragment breakage, microcracks, overlapping cells and surface contamination above 0.5 mm in dimension.
In addition, the pioneering adoption of the SVA intelligent acquisition card drastically cuts hardware resource occupancy of industrial PCs, lowering equipment costs while securing consistent inspection throughput.
02 Final Surface Inspection & Classification (AOI)
This solution performs color grading sorting on both front and back sides of finished solar cells, alongside defect inspection for surface damage, stain spots, poor screen printing and abnormal grid line dimension. It enables sharp imaging of tiny defects as small as 50μm.
Compatible with multiple cell printing formats including PERC, TOPCon MBB, SMBB, 0BB, shingled cell and BC cell technologies, the system satisfies diversified inspection requirements from customers.
Full in-house development of high-efficiency inspection technology builds a multi-dimensional quality assurance system.
Beyond the above-mentioned production processes, Hikrobot also showcases a full lineup of high-performance inspection solutions, including cell surface debris inspection, CIS macro-focus line scan cameras, SC5000X label defect inspection and dynamic testing for smart sensors.
Powered by core technologies such as six-camera opposed ranging, 4K line-scan near-infrared imaging, high-precision 3D vision and 2.5D dome illumination imaging, the system accurately identifies various flaws: silicon wafer scratches and thickness deviations, solar cell microcracks and edge chipping, module packaging defects as well as debris and scratches on battery cell surfaces. Its maximum inspection precision reaches the micron level, forming a robust quality barrier throughout the full lifecycle of PV and energy storage products.
On-site demonstrations also feature solutions spanning the entire PV industrial chain: SC6500 wafer identification, post-printing PL inspection, post-coating appearance inspection, module label laminating & code reading, and industrial cleaning, driving dual upgrades in production capacity and product quality across the photovoltaic sector.
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Featured Honor | Turck TIV AI Smart Camera Wins Readers' Choice Annual Product Award
2026-05-28
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TIV AI Camera Claims Top Honor in Vision Processing Category
The reader selection results of Computer & Automation have been officially released. Turck TIV intelligent vision AI camera has won the 2026 Annual Product Award in the vision category.
The magazine selects around 600 new industrial products every year, from which its editorial team picks 96 high-quality products across 12 major categories for the final selection. The voting page recorded over 19,300 visits, reflecting widespread industry attention to this event. Ultimately, Turck stood out with solid technical strength and claimed the top award.
English Translation
The TIV AI camera stands out with powerful true edge intelligence. This self-learning AI vision camera directly deploys artificial intelligence technology on industrial production lines without any external auxiliary devices. After powering on, it only needs a small number of sample images for simple training to start efficient operation quickly.
Equipped with a 12-megapixel global shutter sensor and NVIDIA GPU, it embeds integrated AI applications including difference detection, classifier, object detector and barcode scanning, fully meeting real-time inspection demands of various high-standard industrial scenarios. Featuring robust structure and strong compatibility, it supports easy integration via M12 interfaces and Turck Automation Suite (TAS).
This award further consolidates Turck’s leading innovative position in the industrial AI sector, and once again reflects the company’s original aspiration to develop intelligent automation and sustainable automation solutions. You can check the official designated platform for the complete winners list and more relevant news of this reader selection activity.
Technical Advantages
Rapid commissioning without programming
IP67 protection grade, standalone operation, no extra edge hardware required
Real-time detection results, expandable via scalable network
With the new TIV AI camera, Turck brings a landmark transformation to industrial image processing. No complex programming is required; users only need to train the smart camera with a few sample images.
As Turck Intelligent Vision Camera, TIV can independently learn patterns and deviations, and reliably distinguish qualified and defective parts as well as different product categories. Neural network training and inference are executed directly on the camera, supported by a high-performance 12MP global shutter sensor (4th-gen Sony Pregius S) and NVIDIA Jetson Nano GPU with 4GB memory, enabling on-device real-time image processing.
Flexible, Efficient and User-Friendly
The TIV12MG-Q110N comes with four pre-installed AI applications: deviation inspection, classification, object detection and code reading, covering core industrial vision tasks ranging from completeness inspection and product classification to target positioning and 1D/2D barcode recognition.
It can independently analyze and evaluate designated inspection areas, and directly output coordinates, confidence scores and pass/fail signals to PLC or IT systems. Featuring standard C-mount interface for flexible lens matching, and optional protective housing to reach IP67 protection grade. Its decentralized and robust design integrating control system, lighting unit, sensor and power supply makes it ideal for direct on-site deployment on production lines.
Seamless Integration with TAS
Intuitive operation is available via web browser. Fully integrated into Turck Automation Suite (TAS), it simplifies device management and facilitates digital maintenance and monitoring workflow.
Equipped with M12 connectors for power supply, network, trigger signal and I/O interfaces, the camera can be easily embedded into existing industrial systems. Local operation, transferable datasets and neural networks support scalable deployment without additional edge computing hardware or extra licensing fees.
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SICK Application | SICK Inspector8512 Smart Camera + AI Powers Candy Residue Inspection on Molds
2026-05-22
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SICK Application
Overview
Inspection of candy residue on molds is a crucial quality control step after candy production. Traditional vision algorithms feature cumbersome debugging and high deployment costs. Equipped with high-resolution imaging, flexible integration capability and powerful built-in AI inspection tools, the SICK Inspector8512 smart camera delivers a stable, accurate and professional solution for candy residue detection on molds.
01 Industry Pain Points
Candy residue inspection on molds is vital to guarantee product quality, demolding effect and food safety during candy manufacturing. Nonetheless, special mold structures and harsh production line conditions bring multiple challenges to conventional vision algorithms:
Diverse shapes of products and molds
Complex mold structures and various candy types result in poor versatility of traditional algorithms. They fail to adapt to diverse scenarios, accompanied by tedious debugging and high adaptation costs.
Low distinguishability due to similar color and texture
Candies share similar color with mold materials with low contrast. Conventional vision algorithms are prone to misjudgment and missing detection, making stable identification of residual candies impossible.
Insufficient equipment integration and compatibility
Multiple industrial communication protocols need to be supported, while traditional devices have limited communication performance. Complicated system configuration leads to difficult and costly deployment and maintenance.
Strict on-site reliability requirements
Inspection results directly affect quality management and production traceability. Changes of workshop temperature, humidity, light and other environmental factors easily undermine the stability of traditional solutions.
02 SICK Inspector8512 Solution
Inspection Requirements
Detect randomly distributed residual candies on molds.
Transmit detection results to PLC via PROFINET communication.
Detection Difficulties
The field of view is wide, while residual candies to be detected are tiny, forming small target detection within a large viewing range.
Residual candies vary in color and size.
Solution Selection
Detection Procedures
After positioning the camera and light source, access the camera software interface via browser. Set proper parameters including exposure time, focal length, contrast and brightness to capture clear images.
Clarity image
Add the AI object detection tool and pre-train the model with samples of residual candies at different positions on molds to identify candy residues on mold surfaces. The Inspector8512 smart camera is equipped with Nova image processing software, which integrates abundant image processing algorithms for users to quickly build customized processing workflows.
Comprehensive image processing algorithms
No OK image of the candy was detected.
Detected the NG image of the candy
Transmit detection results to PLC or host computer via applicable communication modes, including digital IO output signals, TCP/IP, PROFINET and EtherNet/IP. Captured images can also be saved to the host computer through FTP for subsequent traceability and inquiry.
03 Solution Advantages
12-megapixel high-resolution imaging: Equipped with a 12MP high-definition CMOS sensor and external high-performance lighting, it clearly captures residual candies in mold gaps and edges.
Minimalist web visual configuration: Adopting web interface based on SICK Nova platform. No professional programming required, enabling both technical and general staff to quickly set parameters and build inspection solutions.
AI intelligent detection: Adopt AI object detection tool. Accurately identify mold candy residues via sample training without complex rule programming, delivering stable and reliable inspection performance.
Flexible optical adaptation: Standard C-Mount lens interface with manually adjustable focal length. Compatible with various external lenses and lighting modules to meet diverse mold installation and inspection requirements.
Easy industrial integration: Supports mainstream industrial buses including dual-port EtherNet/IP and PROFINET. Combined with high-speed I/O, it can be rapidly connected to production line PLC and control systems.
04 Basic Camera Parameters
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