基于YOLOv8的林区行人目标检测研究  

Forest Pedestrian Detection Based on Improved YOLOv8

作　　者：李琳琳 孙海龙[1] LI Linlin;SUN Hailong(College of Computer and Control Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学计算机与控制工程学院,哈尔滨150040

出　　处：《森林工程》2025年第1期138-150,共13页Forest Engineering

基　　金：黑龙江省应用技术研究与开发计划项目(GA20A301-2)。

摘　　要：为解决目标检测算法在林区行人检测中容易出现漏检及检测精度不足的问题,提出一种基于改进YOLOv8的林区行人目标检测算法。采用C2f_DWRSeg模块替换C2f模块,扩展初始卷积通道数,使网络能更高效地进行多尺度特征提取;提出一种重构的检测头,训练时增加卷积层的复杂性,推理时使用单分支结构,从而丰富网络的特征表示能力,并保持高效的推理速度;在特征融合前增加了卷积注意力机制模块CGA,减少计算量;使用Focaler-ShapeIoU损失函数代替CIoU损失函数,弥补边界框回归方法的不足,进一步提高检测能力。试验结果表明,与基准模型相比,改进后的算法mAP50提高了2%,mAP50-95提高了2.4%,模型的处理速度(FPS)提高了4.33%,证明改进后的算法能够更好地应用在林区行人检测的任务中。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-Shape Io U loss function to replace the CIo U 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 m AP50 has increased by 2%,m AP50-95 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 

分 类 号：S771[农业科学—森林工程]