改进YOLOv5s的落叶树鸟巢检测方法
Improved Deciduous Tree Nest Detection Method Based on YOLOv5s
程萌 1李浩1
作者信息
- 1. 河海大学地球科学与工程学院,江苏 南京 211100
- 折叠
摘要
针对从落叶树上识别鸟巢具有目标较小、背景复杂、目标与背景易混淆等问题,本文提出一种基于YOLOv5s改进的落叶树鸟巢检测模型YOLOv5s-nest.在Backbone中插入改进的注意力机制Bi-CBAM,提升网络对小目标的感知能力;在Neck中引入SDI结构,以融合更多层次特征图和更高级的语义信息;在Neck中插入InceptionNeXt结构,用于提高模型的性能和运算效率;在Head检测头中将普通卷积替换为PConv,可以更高效地提取空间特征及提高检测效率.实验结果表明,改进模型的平均精确率达到了89.1%,相较于原始模型提高了6.8个百分点.
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引用本文复制引用
基金项目
国家自然科学基金资助项目(41471276)
出版年
2024
国家科技期刊平台
NSTL
万方数据