Suyoung Seo

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.