Research on the Recognition of Livestock Images in the Qinghai-Tibet Plateau Based on YOLOv6
Aiming at the problem of high error rate and missed detection rate in animal breed image recog-nition on the Qinghai-Tibet Plateau under occlusion conditions we propose the BC-YOLOv6 algorithm,which combines the backbone of YOLOv6 with the BiFormer of the bidirectional Transformer layer.At the same time,the Coordinate Attention for Effective Mobile Network Design(CA)module in its architecture is intro-duced to improve the recognition rate of small targets,NMS,design relevant functions,and increase recall rate.The experimental results show that BC-YOLOv6 has improved the accuracy,recall,and average accuracy of the original model by 16.6％,8.9％,and 21.9％,respectively.
occlusion issuesYOLOv6livestock breeding in the Qinghai-Tibet Plateau
NETL
NSTL
万方数据
