An improved YOLOv3 algorithm to detect molting in swimming crabs against a complex background

Abstract Traditional methods of breeding soft-shell crabs mainly rely on manual identification, which has high costs regarding manpower and resources. Manual inspection may also interfere with crabs’ molting, causing molting failure, and possibly even death, which is costly and inefficient. This paper combines an improved YOLOv3 algorithm with an adaptive dark-channel defogging algorithm to realize the real-time detection of whether a swimming crab in a single-crab basket-culture system is molting. For learning more features, affine, rotation transformation and local occlusion are used to augment the training data to simulate the difficulty of identification caused by occlusion, in case molting may occur under distorted viewing conditions in real culture environments. A k-means++ clustering algorithm is used to obtain prior boxes matching the size of the carapace throughout the entire breeding cycle, and so improve the Intersection over Union (IOU). The identification network itself can have its network structure pruned and the non-maximum suppression function modified to increase rapidity and accuracy; the improved network can recognize and give early warning of the early stage of molting. The precision of the improved model in clean water reaches 100 %, and the running speed was 31 FPS. In turbid water where the prediction confidence is lower than the cut-in threshold of defogging algorithm set as 0.8, the precision of the improved model was over 91 %, and the speed can still maintain about 7 FPS.