计算机与数字工程2025，Vol.53Issue(3)：701-707,7.DOI:10.3969/j.issn.1672-9722.2025.03.016
基于内卷算子的YOLOv5野生动物检测
YOLOv5 Wildlife Detection Based on Involution Operator
摘要
Abstract
Wildlife is an important part of the natural environment,and its conservation is of great importance to human devel-opment.Nowadays,using infrared cameras with deep learning algorithms to monitor wildlife provides an effective way for biological conservation.In this paper,an infrared wildlife image detection algorithm based on YOLOv5 is designed.This paper introduces the involution operator with feature splicing operation in the neck network part of YOLOv5.The original concat splicing operation of the neck network is improved.Its weighting operation is applied to different feature layers according to the importance of the features,giving higher weights to the important feature layers and making the network focus more on the key information.The improved algo-rithm in this paper has substantial improvement in all metrics compared with the original algorithm,providing a more effective meth-od for wildlife detection.关键词
野生动物检测/YOLOv5/内卷算子/特征融合/特征层加权Key words
wildlife detection/YOLOv5/involution operator/feature fusion/feature weighting分类
信息技术与安全科学引用本文复制引用
贺鹏飞,王菲菲,孙彩惠,聂荣,刘志航..基于内卷算子的YOLOv5野生动物检测[J].计算机与数字工程,2025,53(3):701-707,7.基金项目
烟台市2021年校地融合发展项目(编号:1521001-WL21JY01) （编号:1521001-WL21JY01）
2022年河南省科技攻关项目(编号:222102220048)资助. （编号:222102220048）
