A Dual-Backbone Architecture for Lightweight RT-DETR Based Steel Defect Detection

To address the challenges of model complexity, slow inference speed, and insufficient edge feature extraction in industrial surface defect detection, this paper proposes a lightweight improved RT-DETR-based object detection method. The proposed approach adopts a dual-backbone architecture: Backbone A inherits the deep HGBlock stacking strategy of RT-DETR-l to extract global semantic features, while backbone B is designed using the C2f structure combined with standard convolution modules to supplement shallow edge features. Furthermore, a Local-Global Attention Fusion (LGAF) module is introduced into the backbone to enhance multiscale feature fusion, and a Selective Boundary Aggregation (SBA) module is incorporated into the detection head to strengthen semantic guidance and boundary-aware contextual modeling. Compared with the original RT-DETR-l model, the proposed model achieves a reduction of approximately 47.82\% in the number of parameters and a 60.27\% decrease in FLOPs, while maintaining superior detection performance on both the NEU-DET and APDDD datasets. Experimental results demonstrate that the proposed method effectively reduces computational resource consumption while achieving high detection accuracy, exhibiting strong cross-dataset generalization ability and suitability for deployment in industrial scenarios with stringent speed and resource constraints.