基于机器人和图像识别的长距离输水隧洞的自动巡检系统研究及应用

Research and application of automatic inspection system for long-distance water conveyance tunnels based on robots and image recognition

摘要

【目的】针对长距离水工隧洞人工检测效率低、风险高等问题，研发一套集“自主巡检机器人+深度学习裂缝识别与量化”于一体的水工隧洞智能巡检系统，以实现隧洞病害的精准、高效检测。【方法】该系统通过多传感器融合(机器人集成了激光SLAM、深度视觉、码盘与IMU),以实现在复杂隧洞环境中的自主避障与路径规划，结合RFID无源标签辅助定位与校正，确保机器人移动精度控制在±2 cm。机身搭载智能补光系统与环形拍摄装置，可适应低照度与狭窄空间下的图像采集需求。通过对行走速度(0.6~1.0 m/s)、曝光时间(1.5~4 ms)及相机增益(3~9 dB)等关键参数进行多轮测试与优化，实现对洞壁高清图像的垂直稳定采集。采集图像输入基于改进U-Net的裂缝识别模型CrackARNet,该模型引入残差连接与通道注意力机制，在公开裂缝数据集上优于U-Net、TernausNet与Mask R-CNN等主流模型(IoU提升约4%)。系统可自动输出裂缝预测掩膜、可视化结果，并提取裂缝长度、宽度等形态参数。【结果】试验表明，该系统单次任务检测效率较传统人工方式提升约3倍；CNN算法可识别的最小裂缝宽度为2像素，测量精度达毫米级。【结论】该系统通过多传感器融合与AI算法协同，为长距离水工隧洞的精准导航、高效成像与智能诊断提供了可行的一体化解决方案，具有显著工程应用价值。

Abstract

[Objective] To address the low efficiency and high risks associated with manual inspections of long-distance hydraulic tunnels, an intelligent tunnel inspection system is developed that integrates an autonomous inspection robot with deep learning-based crack identification and quantification, enabling accurate and efficient detection of tunnel defects.[Methods] Through multi-sensor fusion(the robot integrates laser SLAM, depth vision, encoders, and IMU), the system achieves autonomous obstacle avoidance and path planning in complex tunnel environments. Combined with passive RFID tag-assisted positioning and calibration, it ensures robotic movement accuracy within ±2 cm. The robot body is equipped with an intelligent fill-light system and a ring-shaped imaging device, enabling image capture in low-light and confined spaces. Through multiple rounds of testing and optimization of key parameters—including travel speed(0.6~1.0 m/s), exposure time(1.5~4 ms), and camera gain(3~9 dB)—the system achieves stable vertical acquisition of high-definition tunnel wall imagery. Captured images feed into CrackARNet, a crack detection model based on an enhanced U-Net architecture. This model incorporates residual connections and channel attention mechanisms, outperforming mainstream models like U-Net, TernausNet, and Mask R-CNN on public crack datasets(with approximately 4% improvement in IoU). The system automatically outputs crack prediction masks, visualizes result, and extracts morphological parameters such as crack length and width.[Results] Experiments demonstrate that the system achieves approximately threefold improvement in single-task detection efficiency compared to traditional manual method. The CNN algorithm can identify cracks as narrow as 2 pixels, with millimeter-level measurement accuracy.[Conclusion] By integrating multi-sensor fusion and AI algorithms, this system provides a feasible integrated solution for precise navigation, efficient imaging, and intelligent diagnostics in long-distance hydraulic tunnels, offering significant engineering application value.

关键词

水工隧洞 / 巡检机器人 / 智能补光 / 自主导航 / 裂缝识别 / 深度学习 / 智慧运维 / 多源感知

Key words

hydraulic tunnel / inspection robot / intelligent supplementary lighting / autonomous navigation / crack identification / deep learning / intelligent operation and maintenance / multi-source perception

基金资助

国家自然科学基金项目(52179105)

犬木塘水库工程科技创新项目(QMT-KY/05-2022)

湖南省水利科技项目资助(XSKJ2022068-07)

湖南省水利科技项目资助(XSKJ2025056-07)

湖南省教育厅优秀青年项目(22B1005)