摘要
针对道路缺陷小目标在复杂背景下检测精度低、漏检误检率高、泛化能力欠佳的问题,提出了一种改进YOLOv8的道路缺陷检测算法SGBNet。首先,Neck部分用加权双向特征金字塔网络(Bi-directional Feature Pyramid Network, BiFPN)替换PANet,提升模型的特征融合能力;其次,Neck引入全局注意力机制(Global Attention Machanism, GAM),在特征融合阶段进行注意力调整,提高检测精度;最后,添加小目标检测层,进一步增强深层语义信息与浅层语义信息的结合,提高对道路缺陷小目标的检测能力。与原始YOLOv8n算法相比,算法SGBNet的精确率、召回率和平均精度分别提升了3.3%, 2.5%和2.5%,实现了对道路缺陷更精准的检测。
A road defect detection algorithm SGBNet,which improves YOLOv8,is proposed to address the issues of low detection accuracy,high missed and false detection rates,and poor generalization ability of small targets with road defects in complex backgrounds.Firstly,the Neck part replaced PANet with BiFPN weighted bidirectional feature pyramid to improve the feature fusion ability of the model.Secondly,GAM is introduced into Neck to adjust attention in the feature fusion stage and improve the detection accuracy.Finally,a small target detection layer is added to further enhance the combination of deep semantic information and shallow semantic information.and improve the ability to detect small objects with road defects.Compared with the original YOLOv8n algorithm,the accuracy,recall,and average accuracy of the algorithm SGBNet has been improved by 3.3%,2.5%,and 2.5%,respectively,achieving more accurate detection of road defects.
作者
李昊璇
苏艳琼
LI Haoxuan;SU Yanqiong(College of Physics and Electronic Engineering,Shanxi University,Taiyuan 030006,China)
出处
《测试技术学报》
2024年第5期506-512,共7页
Journal of Test and Measurement Technology
