水利水电技术（中英文）2026，Vol.57Issue(7)：209-226,18.DOI:10.13928/j.cnki.wrahe.2026.07.016
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
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分类
建筑与水利引用本文复制引用
黄跃群,李金友,袁祯,刘耀儒,蒋买勇,谢穆武,周柏林,陈理..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[J].水利水电技术（中英文）,2026,57(7):209-226,18.基金项目
国家自然科学基金项目(52179105) （52179105）
犬木塘水库工程科技创新项目(QMT-KY/05-2022) （QMT-KY/05-2022）
湖南省水利科技项目资助(XSKJ2022068-07,XSKJ2025056-07) （XSKJ2022068-07,XSKJ2025056-07）
湖南省教育厅优秀青年项目(22B1005) （22B1005）
