Autonomous Pipeline Inspection Using Lightweight Vision-Based UAV Tracking

Abstract

Long-range pipelines are indispensable infrastructures requiring continuous and rigorous inspections for structural health monitoring. Multicopter unmanned aerial vehicle (UAV) inspections face critical challenges including low-altitude flight constraints, limited computational resources for integrated visual perception and navigation control for the purpose of continuous inspection in real-time. The present work proposes an autonomous vision-based UAV low altitude inspection framework for industrial pipeline inspection. The proposed framework seamlessly integrates three innovative components: a lightweight deep learning architecture (MLA-Net) for robust pipeline segmentation, a vision-based control strategy for smooth tracking, and a sliding mode controller for real-time navigation. MLA-Net combines cascaded feature extractor (CFE) blocks for hierarchical feature learning with multi-scale receptive (MSR) blocks for multi-scale environmental adaption, achieving efficient pipe detection under varying conditions. The vision-based control strategy processes MLA-Net outputs through perspective projection and oriented rectangle extraction to coordinate yaw-lateral control, while a nonlinear forward velocity function prevents overshooting during pipeline turns. The sliding mode controller fuses visual features with altitude data for autonomous navigation across complex pipeline networks. Extensive validation through simulation and real-time outdoor experiments demonstrates superior performance across diverse pipeline configurations including intersections and multipipe scenarios. The proposed MLA-Net achieves 0.8166 IoU while operating at $\approx$27 FPS with only 0.202 GFLOPs and 0.178 M, significantly outperforming existing methods in computational efficiency. The integrated framework enables stable autonomous tracking across various pipeline geometries, providing a practical solution with enhanced accuracy and computational efficiency for resource-constrained UAV applications. Source code available at Autonomous vision-based pipline tracking by UAV.