Multi-object detection and behavior tracking of sea cucumbers with skin ulceration syndrome based on deep learning

Abstract

Skin ulceration syndrome of sea cucumbers is one of the most serious diseases in intensive aquaculture, and it is the most effective way of preventing the spread of this disease to detect the abnormal behavior of sea cucumbers in time and take corresponding measures. However, the detection and tracking of multi-object is a hard problem in sea cucumber behavior analysis. To solve this problem, this paper first proposes a novel one-stage algorithm SUS-YOLOv5 for multi-object detection and tracking of sea cucumbers. The proposed SUS-YOLOv5 optimizes the maximum suppression algorithm in the overlapping region of the object detection box. Next, the SE-BiFPN feature fusion structure is proposed to enhance the transmission efficiency of feature information between deep and shallow layers of the network. Then, a MO-Tracking algorithm is proposed integrated with DeepSORT to achieve real-time multi-object tracking. Experimental results show that the mAP@0.5 and mAP@0.5:0.95 of the proposed object detector reach 95.40% and 83.80%, respectively, which are 3.30% and 4.10% higher than the original YOLOv5s. Compared with the traditional SSD, YOLOv3, and YOLOv4, the mAP of SUS-YOLOv5 is improved by 5.49%, 1.57%, and 3.76%, respectively. This research can realize the multi-object detection and tracking, which lays the foundation for the prediction of skin ulceration syndrome in sea cucumbers and has a certain practical application value for improving the intelligence level of aquaculture.