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I am trying to find references on how the resolution of an object affects the ability of object detection systems such as MaskRCNN and YOLO to correctly identify the object.

For example, if the camera is zoomed further and further out, the number of pixels making up the object will shrink, and eventually the object will occupy just a single pixel. At this point the algorithm can only use the values of that single pixel, and so it seems unlikely that even a very accurate algorithm will be able to make a detection. I'm hoping to find any sort of reference for how the performance degrades as the pixels per object are decreased.

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Most object detection/segmentation papers report AP-S, AP-M, and AP-L corresponding to the AP for small, medium, or large objects. As expected, the AP-L is almost always the highest, and AP-S the lowest. You can find precise definitions of AP-S/M/L on the COCO website.

I'm not aware of any more fine-grained study.

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