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OA

[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.