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I work with OpenCV 249. When I downsize whole image with,say, k=0.6 koefficient (both width and height redused proportionally), I do the same with parameter minSize=minSize(minSize_original_widthk, minSize_original_heightk), the same with maxSize. I visualize detecting result. The detection rate of bigger objects stay the same in reduced image, but smaller objects are not detected anymore as they were in the original, larger image. Is there any other, hidden constraint, which I don't know? I use pre-trained model.

I work with OpenCV 249. When I downsize whole image with,say, k=0.6 koefficient (both width and height redused proportionally), I do the same with parameter minSize=minSize(minSize_original_widthk, minSize_original_heightk), the same with maxSize. I visualize detecting result. The detection rate of bigger objects stay the same in reduced image, but smaller objects are not detected anymore as they were in the original, larger image. Is there any other, hidden constraint, which I don't know? I use pre-trained model.

I work with OpenCV 249. When I downsize whole image with,say, k=0.6 koefficient (both width and height redused reduced proportionally), I do the same with parameter minSize=minSize(minSize_original_widthminSize=minSize(minSize_original_width x k, minSize_original_heightminSize_original_height x k), the same with maxSize. I visualize detecting result. The detection rate of bigger objects stay the same in reduced image, but smaller objects are not detected anymore as they were in the original, larger image. Is there any other, hidden constraint, which I don't know? I use pre-trained model.

I work with OpenCV 249. 2.4.9. When I downsize whole image with,say, k=0.6 with, lets say, k=0.6 koefficient (both width and height reduced proportionally), I do the same with parameter minSize=minSize(minSize_original_width x k, minSize_original_height x k), k), the same with maxSize. maxSize.

I visualize detecting result. The detection rate of bigger objects stay the same in reduced image, but smaller objects are not detected anymore as they were in the original, larger image.

Is there any other, hidden constraint, which I don't know? I use pre-trained model.