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

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.

Keywords

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