To understand their correlation, it is essential to clarify their core functions. The frame rate defines the capture speed, while exposure determines the light intake volume. Together, they govern the temporal resolution, brightness and overall clarity of acquired images.
Frame rate, measured in frames per second (fps), refers to the number of images a camera captures every second. It serves as the key metric to judge whether a vision system can keep pace with moving targets. In high-speed scenarios, insufficient frame rates directly cause frame loss or motion smear. For instance, if a conveyor belt carrying electronic components runs at 1 m/s and the camera operates at only 20 fps, the component will move 50 mm between each frame interval — far exceeding the standard detection tolerance of ±0.1 mm. In this case, the frame rate needs to be raised above 100 fps to limit the moving distance within 10 mm per frame.
Nevertheless, a higher frame rate does not always mean better performance. On the one hand, increasing the frame rate compresses the maximum exposure time per frame. Theoretically, a 30 fps camera allows a maximum exposure time of 33 ms, while a 60 fps configuration cuts the maximum exposure time in half to 16.5 ms. On the other hand, high frame rates demand greater image transmission bandwidth. For example, a GigE camera running at 100 fps with 2-megapixel resolution operates at nearly full bandwidth capacity, which may lead to data packet loss.
Exposure time, also known as shutter speed, refers to the duration for which the sensor receives light, measured in milliseconds (ms) or microseconds (μs). It determines the light volume captured for each single frame and is particularly critical for low-light environments and high-reflection scenarios.
In short, the relationship between frame rate and exposure is essentially a trade-off between real-time performance and image quality. To achieve real-time capture for high-speed scenarios, partial exposure time must be sacrificed, which may darken images. To guarantee high clarity for static precision measurement, the frame rate has to be reduced to extend exposure time, which may result in lost dynamic details.
II. Calculation Logic of Frame Rate and Exposure: From Theoretical Formulas to Practical Constraints
Engineers usually estimate the correlation between frame rate and exposure through basic formulas. However, theoretical calculation serves only as a fundamental reference. In practical applications, parameters must be adjusted according to inherent hardware characteristics of industrial cameras, such as readout time and interface bandwidth.
1. Basic Calculation Formula: Shutter Time = 1 / Frame Rate
This is the most simplified theoretical logic. If a camera captures F frames per second, the maximum available time for each frame is theoretically 1/F. That is, the exposure time cannot exceed 1/F to ensure full frame collection within one second.
Examples:
At 30 fps, the theoretical maximum exposure time = 1/30 ≈ 0.033 s = 33 ms;
At 100 fps, the theoretical maximum exposure time = 1/100 = 0.01 s = 10 ms.
It is critical to note that this formula only applies to ideal conditions with zero readout time and zero transmission delay. In actual operation, after completing exposure, the camera sensor needs to read out charge signals and transmit data to the computer, which consumes part of the single-frame cycle. For example, if an area-scan camera has a readout time of 5 ms and runs at 30 fps (single-frame cycle ≈ 33 ms), the actual maximum exposure time is 33 ms – 5 ms = 28 ms, rather than the theoretical 33 ms.
2. Practical Factors Restricting Frame Rate
Frame rate cannot be simply derived from exposure time alone, as it is constrained by multiple hardware and transmission factors:
Sensor Readout Time: Area-scan cameras read pixel signals row by row or column by column. Higher resolution brings longer readout time. For instance, a 5-megapixel camera has a readout time of approximately 8 ms, while a 20-megapixel model requires more than 15 ms.
Data Transmission Bandwidth: Camera interfaces including USB3.0, GigE and CoaXPress determine data transmission speed. Taking GigE interface (maximum bandwidth of 1000 Mbps) as an example: for a 2-megapixel 8-bit grayscale image (about 2 MB per frame), the maximum transmissible frame rate is approximately 62.5 fps. Even with ultra-short exposure time, the frame rate cannot exceed this limit.
Onboard Image Processing: Some cameras perform real-time noise reduction and white balance adjustment, which consumes additional processing time and further lowers the actual frame rate.
Therefore, the maximum frame rate specified in the official camera datasheet is the most reliable reference. For example, the Baumer VCXG.2-25M camera clearly states a maximum frame rate of 53 fps at full 1920*1200 resolution. This value comprehensively accounts for readout time and bandwidth limitations, and shall be used as the benchmark for exposure configuration.
III. Practical Case: Frame Rate and Exposure Calculation of Baumer Cameras
This section takes the Baumer VCXG.2-25M industrial camera equipped with the ON Semiconductor PYTHON2000 sensor as a practical case to verify the dynamic correlation between frame rate and exposure, providing actionable guidance for field engineering deployment.
1. Core Camera Specifications
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Sensor Type: Global shutter CMOS, ideal for high-speed motion scenarios and free from rolling-shutter jello effect
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Sensor Size: 2/3 inch (11 mm diagonal)
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Resolution: 1920*1200 (full frame), pixel size: 4.8 μm * 4.8 μm
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Official Maximum Frame Rate: 53 fps at full resolution (uncompressed GigE transmission)
2. Calculation 1: Maximum Exposure Time at Full Resolution
Single-frame cycle = 1 / maximum frame rate = 1 / 53 fps ≈ 0.01887 s = 18870 μs.
With a built-in readout time of 1200 μs (datasheet implicit parameter), the actual maximum exposure time is calculated as:
Actual maximum exposure time = Single-frame cycle – Readout time ≈ 18870 μs – 1200 μs = 17670 μs (approximately 17.7 ms).
If the exposure time is manually set to 20000 μs (20 ms), exceeding the valid single-frame cycle, the camera will automatically reduce the frame rate for adaptation:
Actual frame rate = 1 / (Exposure time + Readout time) = 1 / (20000 μs + 1200 μs) ≈ 47 fps, lower than the official maximum 53 fps.
3. Calculation 2: Actual Frame Rate Under Different Exposure Durations
Two hypothetical scenarios verify the impact of exposure time on frame rate (transmission delay ignored, only exposure and readout time counted):
Scenario 1: Exposure time = 50000 μs (50 ms)
Actual frame rate = 1 / (50000 μs + 1200 μs) ≈ 19.5 fps (around 20 fps), only 37% of the maximum rated frame rate.
Scenario 2: Exposure time = 100000 μs (100 ms)
Actual frame rate = 1 / (100000 μs + 1200 μs) ≈ 9.9 fps (around 10 fps), only 19% of the maximum rated frame rate.
The calculation results confirm a core rule: within hardware limitations, longer exposure time leads to lower actual frame rate. To maintain high-speed capturing performance, the exposure time must be strictly controlled within the range of single-frame cycle minus readout time.
IV. Common Misconceptions Essential for Engineers
Many engineers misunderstand the calculation logic of frame rate and exposure during on-site debugging. The key clarifications are listed below.
1. Can Frame Rate Be Calculated Directly From Exposure Time?
No. As mentioned above, frame rate is affected by exposure time, sensor readout time, transmission bandwidth and internal processing time. Exposure time alone cannot support accurate frame rate calculation.
For example, two cameras with the same 10 ms exposure time deliver distinct performance: Camera A with 5 ms readout time achieves 66.7 fps, while Camera B with 10 ms readout time only reaches 50 fps.
Correct Configuration Logic: Refer to the official “maximum frame rate vs resolution" datasheet. If the set exposure time ≤ single-frame cycle – readout time, the camera runs at the official maximum frame rate. If the exposure time exceeds the valid range, the actual frame rate equals 1 / (exposure time + readout time).
2. Differences Between Global Shutter and Rolling Shutter
Shutter types bring significant differences to frame rate and exposure matching.
Global Shutter Cameras (e.g., the Baumer model above) expose and read all pixels simultaneously with fixed readout time, enabling stable and predictable frame rate-exposure calculation.
Rolling Shutter Cameras (common in low-cost CMOS sensors) expose and read pixels line by line. Overlapping exposure and readout processes deliver higher frame rates under identical parameters, but easily cause the jello effect and distortion for high-speed moving objects.
Example: A 1920*1080 rolling shutter camera with 8 ms readout time and 10 ms exposure time achieves an actual frame rate of approximately 100 fps, calculated as 1 / max(exposure time, readout time). While offering higher frame rates, rolling shutter solutions are only applicable for static or low-speed inspection scenarios.
V. Solutions for Underexposure: From Hardware Tuning to Scene Adaptation
When high frame rates (required for high-speed inspection) force short exposure times and result in dark images, engineers can adopt this three-level optimization framework to boost brightness while minimizing image quality degradation.
1. Level 1 Optimization: Strengthen Lighting (Priority Option)
A well-accepted consensus in machine vision states that lighting determines imaging quality. Sufficient illumination is the fundamental way to raise brightness without introducing extra noise. Practical measures:
- Increase light source power: For example, raising the power of an LED ring light from 50W to 100W doubles luminous intensity, improving image brightness without extending exposure time.
- Select appropriate light types: For highly reflective workpieces such as metal parts, use low-angle dark-field lighting to avoid overexposure from reflections. For transparent materials like glass, adopt backlighting to outline contours and improve contrast.
- Enable strobe synchronization: Some industrial light sources support strobe triggering synced with the camera frame rate (e.g., 30 fps matched with 30 Hz strobe). Instantaneous light intensity can reach 20 times that of continuous mode, effectively brightening underexposed scenes.
Case: In high-speed inspection of automotive bearings (50 fps, 15 ms exposure), continuous lighting blurred details on bearing raceways. Switching to 50 Hz synchronized strobe lighting delivered a 15* increase in light intensity, revealing fine raceway scratches with zero motion blur.
2. Level 2 Optimization: Adjust Lens Parameters (Secondary Option)
The lens aperture directly controls light intake and serves as an important auxiliary method to enhance brightness:
- Open the aperture wider: A smaller F-number means higher light throughput. For instance, F1.8 transmits four times more light than F4.0 and brightens images under identical exposure settings.
- Be aware of lens limitations: Fast lenses such as F1.4 have shallow depth of field. For inspections requiring large depth of field (e.g., multi-plane components), balance aperture and depth of field. An aperture-first workflow can be applied: open the aperture to meet brightness requirements first, then slightly reduce frame rate to prolong exposure (e.g., drop to 40 fps with 20 ms exposure) to reconcile depth of field and brightness.
Case: For PCB solder joint inspection requiring large depth of field to cover solder points at varying heights, the original F4.0 lens caused underexposure. Replacing it with an F2.8 lens doubled light throughput, while lowering frame rate to 30 fps (25 ms exposure). This kept solder joints sharp and maintained full-board depth coverage.
3. Level 3 Optimization: Raise Camera Gain (Last Resort)
Camera gain is essentially electronic signal amplification. It brightens images by boosting sensor output signals but also amplifies noise — higher gain produces more prominent grain. This should only be used when lighting and lens adjustments are not feasible.
- Moderate adjustment: Normally keep gain below 20 dB (refer to the camera datasheet; noise characteristics vary widely across models), preventing noise from obscuring inspection features.
- Combine with noise reduction algorithms: Many industrial cameras support adaptive noise reduction to suppress noise at elevated gain. Baumer’s SmartNR algorithm, for example, maintains low noise even at 30 dB gain, suitable for low-light applications.
Case: Outdoor license plate recognition for intelligent traffic (30 fps, 20 ms exposure). Backlighting darkened license plates, and physical constraints prevented larger lights or wider apertures. Camera gain was increased from 10 dB to 25 dB paired with noise reduction, improving character clarity while keeping noise within acceptable limits.
VI. Conclusion: The Art of Balancing Frame Rate and Exposure
Tuning frame rate and exposure for industrial cameras is never mechanical application of theoretical formulas. Instead, it represents dynamic matching between scene requirements and hardware capabilities. The core principles are summarized below:
- High-speed dynamic scenarios (sorting, motion tracking): Prioritize frame rate to satisfy real-time demands (calculate minimum frame rate from target speed). Boost brightness via strobe lighting and large-aperture lenses, then apply moderate gain (controlled within 20 dB).
- Static high-precision scenarios (dimensional measurement, defect detection): Prioritize sufficient exposure to avoid motion blur. Reduce frame rate as needed and use backlight or coaxial lighting to enhance contrast.
- Complex mixed scenarios (multi-station production lines): Use HDR / multi-exposure functions supported by certain industrial cameras. Assign distinct exposure and frame rate settings for each station, switched via PLC linkage to meet requirements across all positions.
Ultimately, engineers follow this workflow: theoretical estimation → preliminary tuning → field testing → iterative optimization to find the optimal parameter set for each application. After all, the core goal of machine vision is not chasing the maximum frame rate or longest exposure, but enabling the camera to stably deliver usable images for the target scene.
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