I will introduce the perspective on resolution in deep learning!
We often receive requests to use high-resolution cameras to improve detection capability. In the case of rule-based image processing, using high-resolution images tends to improve resolution and enhance detection capability, but this is not always the case with deep learning. Below is a brief explanation of the perspective on resolution in deep learning, albeit in a rough manner. Let's consider images like (1) to (3) in Figure 1. (1) Total area 10×10, area of the gray rectangle 4 (2) Total area 20×20, area of the gray rectangle 16 (3) Total area 10×10, area of the gray rectangle 16 The area of the gray rectangle in (2) is four times larger than in (1), but when looking at the ratio of the gray rectangle to the total area, (1) is 4/100 and (2) is 16/400, both representing only 4%. In terms of "ease of detecting the gray rectangle" in deep learning, (1) and (2) are almost the same.
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(3) has the same total area of 10×10 as (1), but the gray rectangle is 4 times larger than (1). In terms of ratio, it becomes 16/100, which is 16%. In deep learning, it is generally easier to detect objects that are larger in relation to the whole, and in this case, the rectangle in (3) can be said to be easier to detect than the rectangle in (1). In deep learning, finding small objects within a large image is inherently difficult. If you want to find small objects from a wide area, it is common to either enlarge the object to a size that can be accurately recognized before capturing the image or to take a high-resolution large image and then process it by splitting the image into sections that are detectable in size.
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