• 图像处理 •

ERCUnet：一种基于U-Net改进的道路裂缝检测模型

1. （1.郑州大学软件学院，河南郑州450001；2.中国科学院空天信息创新研究院，北京100101）
• 出版日期:2022-07-25 发布日期:2022-07-25
• 作者简介:刘宇翔（2000—）,男,河南焦作人,本科生,研究方向:人工智能,大数据分析,图像识别，E-mail: lyx2019@stu.zzu.edu.cn； 佘维（1977—）,男,湖南常德人,教授,博士,研究方向:人工智能,信息安全,能源区块链,E-mail: wshe@zzu.edu.cn; 通信作者：沈占峰（1977—）,男,黑龙江大庆人,研究员,博士,研究方向:高分辨率遥感影像信息提取,深度学习,E-mail: Shenzf@radi.ac.cn; 谭帅（2003—）,男,河南信阳人,本科生,研究方向:物联网,人工智能,E-mail: 2826598617@qq.com。
• 基金资助:
郑州大学大学生创新创业训练计划资助项目(202110459167)； 国家自然科学基金资助项目(41971375); 国家重点研发计划项目(2018YFB0505000)

ERCUnet: An Improved Road Crack Detection Model Based on U-Net

1. (1. School of Software Technology, Zhengzhou University, Zhengzhou 450001, China；
2. Aerospace Information Research Institute, Chinese Academy of Sciences， Beijing 100101, China)
• Online:2022-07-25 Published:2022-07-25

Abstract: Aiming at the problems of traditional road  crack  detection methods,  such as low flexibility and poor universality, refering to the residual design in ResNet and the U-shaped encoding and decoding structure of U-Net model, an improved road crack detection model based on U-Net, named ERCUnet,  is designed. The model takes residual blocks as the main body, and optimizes the number of convolution cores of convolution layers at different depths for crack detection. All residual blocks in the model have the same structure, the overall structure of the model is more neat and simple, with good elasticity and strong structure. The residual structure not only makes the feature fusion more sufficient but also avoids the problem of gradient disappearance of deep convolution neural network. The experiment is conducted on the CrackForest dataset. The 118 labeled pictures of CrackForest are divided into training set and testing set according to the ratio of 5〖DK(〗∶〖DK)〗1. Through a series of data expansion methods, the problem of too little training data is effectively alleviated. The loss function combines cross entropy and F1 score to alleviate the imbalance between positive and negative samples. The final experimental results show that the number of parameters of ERCUnet model is only 13.30% of that of U-Net model, the recall, precision, and F1 are all greater than 70%, and noise rate and accuracy are 29.05% and 99.01% on testing set. ERCUnet-tiny model is obtained by modifying model parameters to confirm the plasticity of ERCUnet, and the number of its parameters is only 2.39% of that of U-Net model, similar effect to U-Net is achieved  on testing set.