I am doing some cat poop research and I tried to use YoloV5 to detect different types of poop in the litter box. I collected about 130 poop pictures (Just poop pictures with no background) and labeled them and use roboflow to get annotations, then I follow colab notebook to train data and get the best.pt file for detection. When I run detection using a ramdom litter box picture, the rectangle just bound the whole image or half of the image instead of marking the poops in that image. Then I tried to labled 3 litter box images (Marked poops inside the litter box ) and do it all over again. But when I run detection using a litter box image. Nothing happened. I am so confused. Is it because poop shapes and color are so different to one and the other and they are irregular objects so it caused the detection didn’t work. By the way, I tried OpenCV Haar Cascade method. It at least give me very good result.  Anyone could give me some clues on how to lable the images and train them? And why the old technique is much better than those state of the art method?
The first picture I attached here was done by YoloV5. The second one was done by Haar Cascade method
Thank you very much for your help

pop3814×572 139 KB


haar_cascade1680×1270 346 KB