REW-DETR: A Lightweight Real-Time Detection Transformer for Bridge Surface Defect Detection

Abstract

Timely detection of bridge surface defects is important for structural safety assessment and inspection efficiency. To address defect scale variation, complex background interference, and the high computational cost of RT-DETR-R50, this study proposes REW-DETR, a lightweight bridge surface defect detector. REW-DETR adopts a coordinated lightweight design integrating efficient feature extraction, multi-level channel recalibration, and localization optimization. RepNCSPELAN4–ADown is employed to reconstruct the backbone and fusion stages, ECA is embedded at the P3–P5 feature levels to enhance channel-wise feature discrimination, and Wise-IoU v3 optimizes bounding-box regression without inference-time overhead. On the test set of the bridge surface defect dataset, REW-DETR achieved Precision, Recall, mAP50, and mAP50:95 values of 87.0%, 70.4%, 73.7%, and 42.4%, respectively. These values exceeded those of RT-DETR-R50 by 3.8, 5.5, 4.0, and 4.5 percentage points, respectively. Meanwhile, the parameters and FLOPs were reduced by approximately 53.3% and 60.6%, respectively, while FPS increased from 51.2 to 70.8. After dataset-specific retraining on the bridge-defects-detection dataset from Roboflow Universe, REW-DETR achieved a Recall of 58.0% and an mAP50 of 55.4% while retaining a lightweight configuration. These results demonstrate an improved accuracy–efficiency trade-off and motivate further evaluation of REW-DETR for UAV-assisted bridge inspection, while onboard inference performance remains to be validated.