UAV-Based Low Altitude Remote Sensing for Concrete Bridge Multi-Category Damage Automatic Detection System

Detecting damage in bridges can be an arduous task, fraught with challenges stemming from the limitations of the inspection environment and the considerable time and resources required for manual acquisition. Moreover, prevalent damage detection methods rely heavily on pixel-level segmentation, rendering it infeasible to classify and locate different damage types accurately. To address these issues, the present study proposes a novel fully automated concrete bridge damage detection system that harnesses the power of unmanned aerial vehicle (UAV) remote sensing technology. The proposed system employs a Swin Transformer-based backbone network, coupled with a multi-scale attention pyramid network featuring a lightweight residual global attention network (LRGA-Net), culminating in unprecedented breakthroughs in terms of speed and accuracy. Comparative analyses reveal that the proposed system outperforms commonly used target detection models, including the YOLOv5-L and YOLOX-L models. The proposed system's robustness in visual inspection results in the real world reinforces its efficacy, ushering in a new paradigm for bridge inspection and maintenance. The study findings underscore the potential of UAV-based inspection as a means of bolstering the efficiency and accuracy of bridge damage detection, highlighting its pivotal role in ensuring the safety and longevity of vital infrastructure.

Automated summary

This study proposes a fully automated concrete bridge damage detection system using unmanned aerial vehicle (UAV) remote sensing technology. The system utilizes a Swin Transformer-based backbone network and a multi-scale attention pyramid network to achieve unprecedented speed and accuracy. Comparative analyses show that the proposed system outperforms commonly used target detection models. The robustness of the proposed system in real-world visual inspection reinforces its efficacy, leading to a new paradigm for bridge inspection and maintenance. The study highlights the potential of UAV-based inspection in enhancing efficiency and accuracy in bridge damage detection, emphasizing its pivotal role in ensuring the safety and longevity of vital infrastructure.