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 disease features are easily confused with the background environmental features. Secondly, RepConv and DBB reparameterization 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 reparameterization module is introduced to solve the problem of head parameter redundancy and improve the feature extraction 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 network 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.

Key words: pavement disease detection, feature extraction, reparameterize, multiscale feature, YOLOv8

摘要： 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 disease features are easily confused with the background environmental features. Secondly, RepConv and DBB reparameterization 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 reparameterization module is introduced to solve the problem of head parameter redundancy and improve the feature extraction 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 network 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, YOLOv8