【摘要】 针对路面病害检测中由于病害形态多样、种类繁多以及背景灰度值相似造成噪声干扰导致识别与分类精度不高的问题，采用卷积神经网络YOLOv5为主干框架，提出一种基于改进YOLOv5的路面病害检测模型YOLOv5l-CBF。引入坐标注意力机制，调整网络的注意力权重使模型对病害纹理特征更加关注，并在主干网络的残差结构中引入Transformer构建BotNet网络结构，在减少参数量的同时提高对病害图像中全局依赖关系的捕捉能力。同时，在颈部网络中构建双向加权特征金字塔网络，学习每个特征层的重要性分布权重，并对提取到的病害特征进行双向交叉尺度连接和加权融合。在真实路面病害数据集上的实验结果表明：与YOLOv5l模型相比，YOLOv5l-CBF模型精度与召回率分别提升7.4和8.7个百分点，mAP达到90.8%，在对多种病害的检测与分类上具有显著的性能优势。更多还原

【Abstract】 To address the problem of poor recognition and classification accuracy in pavement disease detection due to the diverse morphologies,the types of diseases,and the interference of similar background gray scale values,a Convolutional Neural Network(CNN) YOLOv5 is used as the basic framework,and pavement disease detection model YOLOv5l-CBF based on improved YOLOv5 is proposed.The Coordinate Attention(CA) mechanism is introduced in the network,and the attention weight of the network is adjusted to focus on the texture features of the disease.A Transformer is introduced into the residual structure of the backbone network to construct the BotNet network structure,which can reduce the number of parameters and improve the ability to capture globally dependent in disease images.A Bi-directional weighted Feature Pyramid Network(BiFPN) is constructed at the Neck,such that the network detects the important distribution weights of each feature layer,and the extracted disease features will achieve effective bi-directional cross-scale connection and weighted fusion.Experimental results on an actual pavement disease dataset demonstrate that compared with YOLOv5l model,the YOLOv5l-CBF model improves the accuracy and recall of disease identification and classification by 7.4 and 8.7 percentage points,respectively,and the mean Average Precision(mAP) can reach 90.8%.It has significant advantages for detecting and classifying multiple disease types in highway environments.更多还原