AGSAM-Net: UAV route planning and visual guidance model for bridge surface defect detection

Abstract

Crack width is a critical indicator of bridge structural health. This paper proposes a UAV-based method for detecting bridge surface defects and quantifying crack width, aiming to improve efficiency and accuracy. The system integrates a UAV with a visual navigation system to capture high-resolution images (7322 × 5102 pixels) and GPS data, followed by image resolution computation and plane correction. For crack detection and segmentation, we introduce AGSAM-Net, a multi-class semantic segmentation network enhanced with attention gating to accurately identify and segment cracks at the pixel level. The system processes 8064 × 6048 pixel images in 2.4 s, with a detection time of 0.5 s per 540 × 540 pixel crack bounding box. By incorporating distance data, the system achieves over 90% accuracy in crack width quantification across multiple datasets. The study also explores potential collaboration with robotic arms, offering new insights into automated bridge maintenance.