The outbreak of aggregative diseases in the process of sea cucumber cultivation has brought huge economic losses to aquaculture farmers. It is of positive significance to realize intelligent detection of abnormal behavior to avoid the outbreak of aggregative diseases. Therefore, this paper researches the approaches of intelligent recognition and behavior tracking of sea cucumbers. Fusing the Coordinated Attention and Bi-directional Feature Pyramid Network, the DT-YOLOv5 intelligent recognition model is proposed to enhance the representation ability and feature extraction ability. A multi-object behavior tracking approach is presented based on the automatic frame-matching coordinates, which can track multiple objects and calculate the volumes of exercise. The experimental results show that the precision, recall and AP_50:95 are 99.43%, 98.91% and 84.89%, respectively. This research provides a theoretical support for the detection of abnormal behavior of aquatic animals during intensive aquaculture and has potential practical application value for protecting the welfare of sea cucumbers and improving the intelligence level of aquaculture.

海参养殖过程中爆发聚集性病害，给养殖户带来了巨大的经济损失。实现异常行为的智能检测，对于避免聚集性疾病的爆发具有积极意义。因此，本文研究海参的智能识别和行为跟踪方法。融合协调注意力和双向特征金字塔网络，提出DT-YOLOv5智能识别模型，增强表示能力和特征提取能力。提出了一种基于自动帧匹配坐标的多目标行为跟踪方法，可以跟踪多个目标并计算运动量。实验结果表明，准确率、召回率和AP_50:95分别为 99.43%、98.91% 和 84.89%。该研究为集约化水产养殖过程中水生动物异常行为的检测提供了理论支撑，对于保护海参福利、提高水产养殖智能化水平具有潜在的实际应用价值。