A new benchmark shows that camera capture settings and lighting systematically change the performance of image classifiers, object detectors, and VQA models, and that common vision datasets are biased toward narrow exposure ranges.
Active Control of Camera Parameters for Object Detection Algorithms
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abstract
Camera parameters not only play an important role in determining the visual quality of perceived images, but also affect the performance of vision algorithms, for a vision-guided robot. By quantitatively evaluating four object detection algorithms, with respect to varying ambient illumination, shutter speed and voltage gain, it is observed that the performance of the algorithms is highly dependent on these variables. From this observation, a novel active control of camera parameters method is proposed, to make robot vision more robust under different light conditions. Experimental results demonstrate the effectiveness of our proposed approach, which improves the performance of object detection algorithms, compared with the conventional auto-exposure algorithm.
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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks
A new benchmark shows that camera capture settings and lighting systematically change the performance of image classifiers, object detectors, and VQA models, and that common vision datasets are biased toward narrow exposure ranges.