计算机工程与应用2024，Vol.60Issue(5)：191-199,9.DOI:10.3778/j.issn.1002-8331.2309-0354
重参数化YOLOv8路面病害检测算法
Re-Parameterized YOLOv8 Pavement Disease Detection Algorithm
摘要
Abstract
Road disease detection is an important way to ensure people's traffic safety.In order to improve the accuracy of road disease detection and achieve timely and accurate road disease detection,a pavement disease detection model of re-parameterized YOLOv8 is proposed.First of all,CNX2f module is introduced into the backbone network to improve the ability of the network to extract pavement disease features,and effectively solve the problem that the pavement dis-ease features are easily confused with the background environmental features.Secondly,RepConv and DBB reparameter-ization modules are introduced to enhance the capability of multi-scale feature fusion and solve the problem of large scale difference of pavement diseases.At the same time,the shared parameter structure of the head is improved,and RBB repa-rameterization module is introduced to solve the problem of head parameter redundancy and improve the feature extrac-tion capability.Finally,the SPPF_Avg module is introduced to solve the problem of pavement feature loss and enrich the multi-scale feature expression.The experimental results show that the accuracy of the improved road disease detection net-work is 73.3% ,the recall rate is 62.3% and the mAP is 69.3% ,which is 2.6,3.0 and 2.8 percentage points higher than that of the YOLOv8 network,and the detection effect of the model is improved.关键词
路面病害检测/特征提取/重参数化/多尺度特征/YOLOv8Key words
pavement disease detection/feature extraction/reparameterize/multiscale feature/YOLOv8分类
信息技术与安全科学引用本文复制引用
王海群,王炳楠,葛超..重参数化YOLOv8路面病害检测算法[J].计算机工程与应用,2024,60(5):191-199,9.基金项目
河北省自然科学基金(F2021209006). （F2021209006）
