Bridge Surface Defect Detection via Heterogeneous Feature Fusion and Multi-Scale Enhancement

Abstract

Automated bridge surface defect detection is essential for improving the efficiency and objectivity of infrastructure inspection under complex field imaging conditions. This study proposes a Multi-Scale Detection Transformer (MS-DETR), an RT-DETR-based detector that integrates HeteroFusionNet, a multi-objective scale-aware integration network (MOSAIN), and a global attention two-dimensional module (GATM). HeteroFusionNet combines shared shallow feature extraction with heterogeneous dual-branch deep modelling to reduce redundant computation and enhance complementary local and global representations. MOSAIN injects high-resolution shallow details into the P3 feature path to improve small-defect recognition, whereas GATM adapts high-level attention encoding to dense two-dimensional visual features. On the self-built bridge defect dataset, MS-DETR achieved a mAP50 of 65.4% and an F1-score of 0.64, with 14.33 M parameters, 39.1 GFLOPs, and 62.3 FPS on an RTX 4090 GPU. On the RDD (China) road defect dataset and the VisDrone2019 dataset, MS-DETR achieved mAP50 values of 88.9% and an AP50 of 0.487, respectively. These results demonstrate that MS-DETR achieves competitive detection accuracy while maintaining a favorable balance between model complexity and inference speed under the evaluated experimental settings.