森林工程2025，Vol.41Issue(1)：138-150,13.DOI:10.7525/j.issn.1006-8023.2025.01.011
基于YOLOv8的林区行人目标检测研究
Forest Pedestrian Detection Based on Improved YOLOv8
摘要
Abstract
In order to solve the problem that the target detection algorithm is prone to leakage detection and insufficient detection accuracy in pedestrian detection in forest areas,a forest pedestrian target detection algorithm based on improved YOLOv8 is pro-posed.The C2f_DWRSeg module is used to replace the C2f module,and the number of initial convolutional channels is expanded so that the network can extract multi-scale features more efficiently.A reconstructed detector head is proposed to increase the complexity of the convolution layer during training,and a single branch structure is used in inference,so as to enrich the feature representation of the network and maintain efficient inference speed;add CGA,the convolution attention mechanism module,before feature fusion,to reduce the amount of calculation;use the Focaler-ShapeIoU loss function to replace the CIoU loss function to make up for the short-comings of the boundary box regression method and further improve the detection ability.Experimental results show that compared with benchmark model,the improved algorithm mAP50 has increased by 2%,mAP50-95 has increased by 2.4%,and FPS has in-creased by 4.33%.It proves that the improved algorithm can be better applied to the task of pedestrian detection in forest areas.关键词
林区管理/行人检测/YOLOv8/注意力机制/损失函数/改进算法/深度学习/识别Key words
Forest management/pedestrian detection/YOLOv8/attention mechanism/loss function/improved algorithm/deep learning/recognition分类
农业科技引用本文复制引用
李琳琳,孙海龙..基于YOLOv8的林区行人目标检测研究[J].森林工程,2025,41(1):138-150,13.基金项目
黑龙江省应用技术研究与开发计划项目(GA20A301-2). （GA20A301-2）
