基于GRU-CNN网络的隧道裂缝实时检测算法  

Real-time detection algorithm of tunnel cracks based on GRU-CNN

作　　者：文国军[1] 高晓峰 毛宇 程斯一 Wen Guojun;Gao Xiaofeng;Mao Yu;Cheng Siyi(School of Mechanical Engineering and Electronic Information,China University of Geosciences(Wuhan),Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)机械与电子信息学院,武汉430074

出　　处：《地质科技通报》2023年第6期249-256,共8页Bulletin of Geological Science and Technology

基　　金：国家自然科学基金项目(41972325,52205611);湖北省重点研发计划(2020BAB054)。

摘　　要：隧道裂缝严重损害隧道的使用寿命以及行车安全,而传统人工检测方法无法对长隧道中的大量裂缝进行高效精确识别。提出了一种隧道表面裂缝实时检测算法,该方法创新性地将用于文本学习、信号分析的门控循环单元(GRU)模型应用于图像分类中,用于提升隧道裂缝检测速度并保证检测精度。为提高训练效率,首先对裂缝进行预处理将其转换至频域中提取隧道裂缝的关键信息并矩阵重构为一维向量,再利用一维卷积神经网络提取一维向量的深度特征并输入循环神经网络学习深度特征中的序列依存关系,最终实现对隧道裂缝的检测。测试结果表明该模型不仅能降低模型训练参数量和硬件配置需求,同时该模型在精度上能达到99.0%,检测单张图片速度能达到2.1 s,相较于主流的分类检测模型其准确率保持不变,训练时间和预测速度显著提升。最后针对大尺寸隧道裂缝图像开发了检测框架,可实现对大尺寸图像中裂缝信息的有效提取。Tunnel cracks seriously damage the corresponding life time and traffic safety.However,traditional manual detections cannot efficiently and accurately identify a large number of cracks in long tunnels.This paper proposes a real-time detection algorithm for tunnel surface cracks.It innovatively applies the Gate Recurrent Unit(GRU)model for text learning and signal analysis to image classification,improving detection speed and ensuring detection accuracy of tunnel cracks.To enhance training efficiency,the cracks are preprocessed and converted into the frequency domain to extract the key information of tunnel cracks,and the matrix is reconstructed into one-dimensional vectors.Then,one-dimensional convolutional neural network is used to extract the vector depth feature,and recurrent neural networks can learn corresponding sequential dependencies to realize tunnel cracks detection.Test results show that this model can reduce the number of training parameters and hardware configuration requirements.At the same time,the detection accuracy can reach 98.8%,and the detection speed for single image can reach in 2.1 s.Comparing with the mainstream classification detection algorithms,its accuracy remains unchanged,with significantly improvements of both training efficiency and prediction rate respectively.Finally,a detection framework is developed for large-scale tunnel cracks to extract corresponding crack information effectively.

关 键 词：隧道裂缝 实时检测 分类检测 频域处理 

分 类 号：U456[建筑科学—桥梁与隧道工程] TP391.41[交通运输工程—道路与铁道工程]