基于改进U-net模型的路面裂缝智能识别

Automatic Identification of Pavement Crack Using Improved U-net Model

路面裂缝快速检测及响应是道路养护部门的一项重要工作,然而传统的裂缝检测方法耗时且准确度低。因此,本文基于改进后的U-net模型实现对路面裂缝精准地自动识别。结合Canny边缘检测、Otsu阈值分割算法和人为干预手段研发一款半自动标注软件,用以实现路面裂缝的像素级标注。研究以路面2D激光图像为数据集,并在此基础上通过数据增强进行数据集样本扩充,从而构建模型训练原始样本库;在实验分析阶段,使用交叉熵损失函数判断预测值与真实值的误差大小,并结合Adam算法优化模型。研究表明改进后的U-net模型在识别精度及泛化能力上均优于原U-net模型及全连接神经网络模型。该研究将为道路养护管理部门的路面病害快速检测提供技术支撑,从而利于快速响应、采取措施保证路面的行车安全。

Rapid detection of pavement cracks is important for road maintenance and rehabilitation,but the traditional crack detection method is time-consuming,labor-intensive and low accuracy.Therefore,an improved U-net neural network model is proposed in this study.By adjusting the model structure and fine-tuning parameters,the U-net model can accurately and automatically identify pavement cracks.In this paper,a new semi-automatic marking software is developed to label pavement cracks based on Canny edge detection and Otsu segmentation algorithms,and the labeled 2D laser images are used as the training dataset.In addition,data enhancement methods are used to augment the training database.In the experimental stage,the cross-entropy loss function is used to compute error differences between the predicted value and the true value based on Adam optimization algorithm.Findings show that the improved U-net model is better than the original U-net model and the fully connected neural network model in terms of detection accuracy and algorithm robustness.This study provides a solution for the rapid detection of pavement diseases,which will be beneficial to road maintenance management department which can rapidly take corrective measures to ensure road traffic safety.