MESC-DETR: An Improved RT-DETR Algorithm for Steel Surface Defect Detection

Accurate detection of steel surface defects is crucial for ensuring safety and efficiency in steel production. In this study, we propose a multi-scale edge-enhanced structured composite detection Transformer (MESC-DETR) based on the RT-DETR framework for steel surface defect detection. Three primary improvements are introduced, as follows: (1) a Composite-ConvNeXtV2 backbone network architecture is developed, which integrates ConvNeXtV2 networks through a dense higher-level composition (DHLC) method to enhance multi-scale feature extraction capabilities; (2) an edge enhancement module (EEM) is proposed, incorporating a scale sequence feature fusion (SSFF) structure to design an edge-enhanced feature fusion (EEFF) architecture, thereby improving multi-scale defect detection and edge information perception; (3) a novel Focal-MPDIoU loss function is formulated by optimizing focal loss and MPDIoU, which further enhances model convergence speed and localization accuracy. Experimental results demonstrate that on GC10-DET and NEU-DET datasets, the proposed algorithm achieves 7.2% and 3.7% improvements in mean average precision (mAP) at IoU = 0.50, along with 2.9% and 1.5% mAP enhancements under IoU = 0.50:0.95. These findings indicate that MESC-DETR exhibits superior performance in steel surface defect detection, holding significant implications for steel manufacturing processes.