计算机工程与应用2024，Vol.60Issue(17)：179-190,12.DOI:10.3778/j.issn.1002-8331.2404-0288
改进YOLOv8的道路缺陷检测算法
Improved Road Defect Detection Algorithm Based on YOLOv8
摘要
Abstract
Various defects can emerge on the road surface after prolonged use.Failing to promptly detect and repair these defects can significantly reduce the road's lifespan and jeopardize driving safety.Consequently,real-time detection of road defects assumes paramount importance.However,traditional detection methods suffer from sluggish speed and hefty cost requirements.Hence,to tackle these challenges,a novel road detection algorithm called DML-YOLO is proposed,which builds upon the YOLOv8 framework.This algorithm integrates the MultiPath coordinate attention(MPCA)mechanism into the backbone network to enhance feature extraction.Additionally,the C2f-MPDC module is introduced to dynamically adjust the receptive field and improve detection capabilities.Furthermore,the network's neck structure is redesigned,introducing a novel diversity feature pyramid network(DFPN)that reduces model size and fuses low-level feature maps to extract rich,detailed information and elevate the success rate of detecting small targets.Moreover,a lightweight shared convolutional detection head(LSCD head)is meticulously designed to enhance detection efficiency while reducing model size.Ultimately,extensive experimental results demonstrate that DML-YOLO achieves remarkable average detection pre-cision,with mAP@0.5 scores of 89.6%on the RDD2022 dataset and 73.6%on the VOC2007 dataset,surpassing other models tested.Additionally,compared to the YOLOv8 model,DML-YOLO boasts a reduction of 32.37%in parameter count and 14.49%in computational workload,making it highly suitable for deployment in resource-constrained computing environments like embedded systems and mobile devices.关键词
多路聚合注意力机制/道路检测/YOLOv8/共享卷积Key words
MultiPath coordinate attention/road detection/YOLOv8/shared convolutional分类
信息技术与安全科学引用本文复制引用
王雪秋,高焕兵,郏泽萌..改进YOLOv8的道路缺陷检测算法[J].计算机工程与应用,2024,60(17):179-190,12.基金项目
山东省自然科学基金(ZR2022MF267). （ZR2022MF267）
