Novel multiple object tracking method for yellow feather broilers in a flat breeding chamber based on improved YOLOv3 and deep SORT

Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models, a novel method based on machine vision to achieve precise tracking of multiple broilers was proposed in this paper. Broilers' behavior in the breeding environment can be tracked to analyze their behaviors and health status further. An improved YOLOv3 (You Only Look Once v3) algorithm was used as the detector of the Deep SORT (Simple Online and Realtime Tracking) algorithm to realize the multiple object tracking of yellow feather broilers in the flat breeding chamber, which replaced the backbone of YOLOv3 with MobileNetV2 to improve the inference speed of the detection module. The DRSN (Deep Residual Shrinkage Network) was integrated with MobileNetV2 to enhance the feature extraction capability of the network. Moreover, in view of the slight change in the individual size of the yellow feather broiler, the feature fusion network was also redesigned by combining it with the attention mechanism to enable the adaptive learning of the objects' multi-scale features. Compared with traditional YOLOv3, improved YOLOv3 achieves 93.2% mAP (mean Average Precision) and 29 fps (frames per second), representing high-precision real-time detection performance. Furthermore, while the MOTA (Multiple Object Tracking Accuracy) increases from 51% to 54%, the IDSW (Identity Switch) decreases by 62.2% compared with traditional YOLOv3-based objective detectors. The proposed algorithm can provide a technical reference for analyzing the behavioral perception and health status of broilers in the flat breeding environment.