Camera Exposure and Computer Vision

What is Camera Exposure?

Exposure is the amount of light accumulated at the image plane during a single capture, set by the lens aperture, the exposure time, and the scene's luminance.

Exposure is quantified by the Exposure Value (EV). In the APEX system, EV = log₂(N²/t), where N is the F-number and t is the exposure time in seconds. Sensitivity is not part of EV: ISO enters the exposure equation separately, as the speed value Sv. A series of gray patches is a common way to assess how that exposure rendered in a captured image. Exposure is not the "brightest bright" relative to the "darkest dark" of an image: that's dynamic range.

What is Exposure?

Correct exposure is important to all computer vision tasks as any over-exposure or under-exposure results in degradation of contrast and in extreme cases, a complete loss of information. Here is a picture of a simulated vehicle under various exposure values.

Even to the human observer, it is difficult to see details in over or under-exposed images.

What is Camera Exposure Value — Different Camera Exposures

Blasinski & Farrell, "Optimizing Image Acquisition Systems for Autonomous Driving"

Gain and ISO

Machine vision cameras usually have no ISO setting at all. They expose gain: analog gain applied to the pixel signal before the ADC, and digital gain applied to the digitized value afterward. Both scale the signal and the noise riding on it together, so gain brightens an image without adding information to it. Digital gain is the weaker of the two, since it multiplies numbers that have already been quantized.

ISO is the photographic calibration of that same amplifier chain, and it is what the APEX equation carries as Sv. Set the exposure time and the aperture first, add light to the scene if you can, and raise gain last, once a shorter integration time is the only way left to stop motion.

Exposure Has a Major Impact on Computer Vision and Machine Vision

Blasinski et al. demonstrated how the mean average precision of detection networks SSD-MobileNet and ResNet drops significantly with incorrect exposure. Even after training the networks using multiple exposure values, the mean average precision dropped by 25 percentage points when over-exposed by four stops (four EV).

Computer Vision Degradation due to Exposure — Exposure and Machine Vision

References and Related Links Blasinski & Farrell, "Optimizing Image Acquisition Systems for Autonomous Driving"

Trying to Determine Your Camera Requirements?

Use our free web-based AoV Calculator to determine your system's Field of View Requirements. Then, use the M12 Lens calculator to match your requirements with the available lenses. Our Depth of Field Calculator also provides the hyperfocal distance and depth of field for every sensor and lens combination.

We also have a couple of other calculators that many engineers find interesting.