基于高清工业相机的盾构隧道裂缝智能识别算法分析

裂缝一直是隧道病害的重点检测对象，但传统人工巡检仅能通过肉眼发现后记录，人工识别精准度与效率完全取决于个人经验判断，无信息化手段辅助，作业效率识别精度亟待提升。针对以上问题，本文借助高清工业相机成像分辨率高、采集速度快等特点，将高清工业相机部署于轨道车上获取隧道表面裂缝病害信息，大幅提高了隧道裂缝识别效率，将识别精度提升至0.2 mm，同时融入优化的Cascade R-CNN算法，在有监督情况下训练隧道裂缝样本，最终实现了隧道裂缝病害的高效提取，同时研发了一套包含硬件数据采集、数据处理软件、数据管理平台的裂缝病害识别路线，真正意义上破除了识别慢、精度低、靠经验、难管理的技术壁垒。

Analysis of intelligent identification algorithm for shield tunnel cracks based on high-definition industrial camera

Cracks have always been the key detection object of tunnel diseases, but it can only be found by the naked eye and then recorded through the traditional manual inspection. The accuracy and efficiency of manual identification completely depend on personal experience judgment, without the assistance of information technology, so the identification accuracy of operation efficiency needs to be improved. In order to solve the above problems, with the help of high-definition industrial camera imaging by high resolution, fast acquisition speed and other characteristics, the high-definition industrial camera is deployed on the rail car to obtain the tunnel surface crack disease information, which greatly improves the efficiency of tunnel crack recognition, and improves the recognition accuracy by 0.2 mm. At the same time, the optimized cascade R-CNN algorithm is integrated to train tunnel crack samples under supervision. At the same time, we develop a set of crack disease identification route from hardware data acquisition, data processing software and data management platform, which really broke the technical barriers of slow identification, low accuracy, relying on experience and difficult management.