改进U-Net的路面裂缝检测方法  

Improved U-Net Pavement Crack Detection Method

作　　者：张明星 徐健[1] 刘秀平[1] 张勇进 张闯 宁小鸽 ZHANG Mingxing;XU Jian;LIU Xiuping;ZHANG Yongjin;ZHANG Chuang;NING Xiaoge(School of Electronic Information,Xi’an Polytechnic University,Xi’an 710048,China;AVIC Shaanxi Aircraft Industry Group Co.,Ltd.,Hanzhong,Shaanxi 723213,China)

机构地区：[1]西安工程大学电子信息学院,西安710048 [2]中航工业陕西飞机工业(集团)有限责任公司,陕西汉中723213

出　　处：《计算机工程与应用》2024年第24期306-313,共8页Computer Engineering and Applications

基　　金：陕西省科技厅项目(2018GY-173);西安市科技局项目(GXYD7.5)。

摘　　要：针对基础U-Net对路面裂缝分割效果不强,裂缝轮廓分割精细度不够、难以识别狭窄裂缝、分割精度低等问题,提出一种改进U-Net的路面裂缝分割方法。使用改进的ResNet50作为主干网络提取路面裂缝特征,设计了基于注意力机制的特征融合模块改进U-Net的跳跃连接,在解码部分添加特征细化头得到改进的模型。使用自建的路面裂缝数据集对提出的模型与目前先进模型进行比较,并对优化前后的模型做消融实验。实验结果表明,该模型在自建的路面裂缝数据集上的mIoU、Precision、mPA分别达到0.838 1、0.892 8、0.916 9,相比于基线U-Net分别提高0.019、0.016 8、0.023 2,推理速度为40.02 FPS能够满足工程应用的需求。在开源的Crack500数据集中验证了该模型相比于U-Net、DeepLabV3+等网络模型具有更强的性能和泛化能力。Aiming at the weak effect of basic U-Net on pavement crack segmentation,insufficient fineness of crack con-tour segmentation,difficulty in identifying narrow cracks,and low segmentation accuracy,this paper proposes an improved U-Net-based pavement crack segmentation method.Firstly,the improved ResNet50 is used as the backbone network to extract the pavement crack features,secondly,an attention mechanism-based feature fusion module is designed to improve the jump connection of U-Net,and finally,the improved model is obtained by adding the feature refinement head in the decoding part.The self-built pavement crack dataset is used to compare the proposed model with the current state-of-the-art models,and ablation experiments are done for the model before and after optimization.The experimental results show that the mIoU,Precision,and mPA of the proposed model on the self-built pavement crack dataset reach 0.8381,0.8928,and 0.9169,respectively,which are 0.019,0.0168,and 0.0232 higher than the baseline U-Net,and the inference speed of 40.02 FPS can meet the needs of engineering applications.Finally,it is verified in the open-source Crack500 dataset that the model in this paper has stronger performance and generalization ability compared with network models such as U-Net and DeepLabV3+.

关 键 词：计算机应用 路面裂缝检测 深度学习 特征融合 语义分割 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]