基于改进YOLOv5s的道路裂缝检测算法
Road crack detection algorithm based on improved YOLOv5s
任安虎 1姜子渊 1马晨浩1
作者信息
- 1. 西安工业大学电子信息工程学院,西安 710021
- 折叠
摘要
为了解决道路巡检系统光学传感器采集的裂缝图像中颜色特征不明显且尺寸不规则造成检测精度不高、泛化能力不足的问题,提出改进YOLOv5s的裂缝检测算法.将结合深度可分离卷积(Depthwise Separa-ble Convolution,DSC)的全局注意力(Global Attention Mechanism,GAM)引入主干特征提取网络,在降低注意力复杂度的同时获得丰富的跨维度特征,增强了裂缝的识别能力;采用空间金字塔软池化网络(Spatial Pyramid Softpool,SPSF),通过Softpool池化保留多维语义以减少信息弥散,提高了边界框回归的准确性;在颈部特征增强网络,运用空洞深度可分离卷积(Atrous DSC)进行下采样,通过扩大感受野加强深层和浅层信息的聚合能力,提高裂缝识别的泛化性.经过在自制道路裂缝数据集上的实验,相较于YOLOv5s,改进算法的mAP提高2.2％,有效提升了道路裂缝检测的准确性和对不同背景下裂缝识别的泛化能力.
Abstract
Aiming at the problems of low detection accuracy and insufficient generalization ability caused by incon-spicuous color features and irregular size in crack images collected by optical sensors in road inspection systems,it is propose improved YOLOv5s crack detection algorithm.The Global Attention Mechanism(GAM)fused with Depthwise Separable Convolution(DSC)is introduced into the backbone feature extraction network to obtain rich cross-dimen-sional features while reducing the complexity of attention.The recognition ability of cracks is enhanced;the spatial pyramid soft pooling network(Spatial Pyramid Softpool,SPSF)is used to preserve multi-dimensional semantics through Softpool pooling to reduce information dispersion and improve the accuracy of bounding box regression;in the neck feature enhancement network,Downsampling is performed with Atrous Depth Separable Convolution(Atrous DSC),which enhances the aggregation ability of deep and shallow information by expanding the receptive field,and improves the generalization of crack identification.After experiments on the self-made road crack data set,compared with YOLOv5s,the mAP of improved algorithm is increased by 2.2％,which effectively improves the accuracy of road crack detection and the generalization ability of crack recognition under different backgrounds.
关键词
道路裂缝检测/YOLOv5s算法/全局注意力机制/深度可分离卷积/Softpool池化Key words
road crack detection/YOLOv5s algorithm/global attention mechanism/depthwise separable convolu-tion/Softpool pooling引用本文复制引用
基金项目
陕西省重点研发计划(2023YBGY031)
出版年
2024
NETL
NSTL
万方数据