Intelligent detection and behavior tracking under ammonia nitrogen stress

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is developed, which is concerned with the object and key points detection and behavior tracking problem for Oplegnathus punctatus in the ammonia nitrogen environment. The proposed model can adapt to the posture change of the object by deforming the receptive field, which solves the problem of false and missed detection caused by the movement and occlusion. Moreover, a new multi-object multi-category tracking algorithm (MOMC-Tracking) is proposed to track and plot the trajectory and calculate the key behavioral characteristics parameters. In addition, an executable software which integrates the proposed DCN-YOLOv5 model and the MOMC-Tracking algorithm is proposed. Extensive experiments show that compared with the typical YOLO series of algorithms, the proposed model in this paper performs the best with the highest accuracy and the fastest convergence speed, where the mAP @0.5 and mAP@0.5:0.95 of the proposed DCN-YOLOv5 model are 93.71% and 57.45%, which are respectively improved by 1.78% and 24.77% as compared with those obtained by the original YOLOv5 model.

Automated summary

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is proposed to address the object detection and behavior tracking problem in the ammonia nitrogen environment. By deforming the receptive field, the proposed model can adapt to the posture change of the object, thereby solving the problem of false and missed detection caused by movement and occlusion. A new multi-object multi-category tracking algorithm (MOMC-Tracking) is also proposed to track and calculate key behavioral characteristics parameters. Experimental results show that the proposed DCN-YOLOv5 model outperforms the typical YOLO series of algorithms in terms of accuracy and convergence speed.