计算机与现代化Issue(8)：24-29,42,7.DOI:10.3969/j.issn.1006-2475.2024.08.005
改进YOLOv5s的落叶树鸟巢检测方法
Improved Deciduous Tree Nest Detection Method Based on YOLOv5s
摘要
Abstract
To address the difficulty of detecting small bird nest targets in complex backgrounds,an improved YOLOv5s network architecture named YOLOv5s-nest is proposed.YOLOv5s-nest incorporates several enhancements:a refined attention mecha-nism called Bi-CBAM is inserted into the Backbone to effectively enhance the network's perception of small targets;the SDI structure is introduced into the Neck to integrate more hierarchical feature maps and higher-level semantic information;the In-ceptionNeXt structure is inserted into the Neck to improve the model's performance and computational efficiency;and in the de-tection head,ordinary convolutions are replaced with PConv to efficiently extract spatial features and enhance detection effi-ciency.The experimental results show that the average precision of the improved model reached 89.1%,representing an increase of 6.8 percentage points compared to the original model.关键词
落叶树/鸟巢识别/无人机影像/深度学习/目标检测Key words
deciduous trees/nest recognition/UAV image/deep learning/object detection分类
信息技术与安全科学引用本文复制引用
程萌,李浩..改进YOLOv5s的落叶树鸟巢检测方法[J].计算机与现代化,2024,(8):24-29,42,7.基金项目
国家自然科学基金资助项目(41471276) （41471276）
