基于YOLOv6的青藏高原畜种图像的识别研究
Research on the Recognition of Livestock Images in the Qinghai-Tibet Plateau Based on YOLOv6
杨琴 1安见才让1
作者信息
- 1. 青海民族大学计算机学院,西宁,810007
- 折叠
摘要
针对遮挡条件下的青藏高原畜种图像识别错误率高、漏检率高的问题,本文提出BC-YOLOv6算法,将YOLOv6的Backbone与双向结构的Transformer层的BiFormer相结合,同时在其架构中引入坐标注意力机制模块(CA),提高对小目标的识别率,改进非极大值抑制(NMS),设计相关函数,提高召回率.实验结果表明,BC-YOLOv6比原模型的精度、召回率、平均精度均值分别提高了16.6％、8.9％、21.9％,有效解决了遮挡问题.
Abstract
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.
关键词
遮挡问题/YOLOv6/青藏高原畜种Key words
occlusion issues/YOLOv6/livestock breeding in the Qinghai-Tibet Plateau引用本文复制引用
基金项目
青海民族大学校级创新项目(09M2022003)
出版年
2024
NETL
NSTL
万方数据