【摘要】 路面破损是影响路面性能的主要因素，其中路面裂缝又是破损中最主要的病害形式，所以，准确检测裂缝并进行及时修复是道路养护中的一个重要课题。采用深度学习的方法，提出了一种路面破损自动检测识别的综合模型。运用路面破检测识别网络对破损裂缝进行检测识别，分类后获得裂缝的类别置信度；利用改进的破损分割网络对路面破损裂缝进行精确分割；结合两种网络的优势提出路面破损检测识别综合模型。整体模型通过优化模型参数和改进网络结构，提高了路面破损的分类精确度和分割效果。与传统的单一模型相比，提出的综合模型不仅能提供路面破损的类别信息，还能提取路面主要裂缝的骨架信息，对于路面养护工作有一定的实用价值。更多还原

【Abstract】 The pavement damage is the main factor affecting the pavement performance, and the pavement crack is the most important form of damage, therefore, the accurate detection of cracks and timely repair is an important topic in the road maintenance. Based on the deep learning method, a comprehensive model for automatic detection and identification of pavement damage is proposed. Firstly, the pavement crack detection and identification network was used to detect and identify the damaged cracks, and the category confidence of cracks was obtained after classification; Then, the improved damage segmentation network was used to segment the pavement damage cracks accurately; Finally, combined with the advantages of the two networks, a comprehensive model of pavement damage detection and identification was proposed. By optimizing the model parameters and improving the network structure, the classification accuracy and segmentation effect of pavement damage were improved. Compared with the traditional single model, the proposed comprehensive model can not only provide the classification information of pavement damage, but also provide accurate location information, which has a certain practical value for pavement maintenance work更多还原