A Vision-Guided Active Crack Alignment Framework for Small-Diameter Pipe Inspection Robots

Inspection inside small-diameter pipelines is difficult because the narrow interior space limits the field of view of onboard cameras. Even when a crack is successfully detected, it may still appear near the image boundary rather than in a suitable position for observation. To address this issue, this study proposes a vision-guided active crack alignment framework for small-diameter pipe inspection robots. The proposed framework uses a YOLOv5s detector to identify the crack region and extract the center of the detected bounding box. The positional difference between the crack center and the image center is defined as the image-plane alignment error. After low-pass filtering, this error is converted into actuator-side reference input through a pixel-to-motor mapping, and a PID-based closed-loop controller is used to regulate a local camera adjustment mechanism so that the detected crack region moves toward the image center. The framework is evaluated mainly through simulation, including controller comparison, different initial offset conditions, parameter sensitivity analysis, robustness tests under visual fluctuation and mapping uncertainty, and an ablation study. The controller comparison shows that all tested PID-based controllers achieve stable convergence, while the fuzzy PID controller provides the best overall performance among the tested cases in terms of settling time, steady-state error, and RMS error. The framework also remains stable under different crack positions and moderate uncertainty conditions. In addition, a preliminary laboratory-scale physical consistency test is conducted to examine whether the convergence tendency observed in simulation can also be reproduced under real visual feedback and actuator response. The preliminary physical results show a convergence tendency consistent with the simulation trend, thereby providing initial support for the practical implementability of the proposed detection-driven alignment concept. Complete integration with an in-pipe robot platform and validation under realistic pipe environments remain future work.