CDCM-DETR: cross-domain consistency-modulated fusion network for steel surface defect detection

Abstract Surface defects in steel exhibit diverse morphologies and significant size variations, often accompanied by low contrast and complex background interference, presenting substantial challenges for detection tasks. To address this, we propose a cross-domain modulation-based end-to-end detection network, CDCM-DETR, to achieve efficient and precise identification of complex defects. First, we design the echo refinement guided network as the backbone, it employs an adaptive joint screening module to perceive defect regions while mitigating deep information decay. Then, we construct the Cross-Domain Modulation and Fusion architecture. It enhances high-frequency responses through frequency-domain compensation using the spectrum resampling alignment unit and strengthens the hierarchical expression of features in the spatial domain by building multi-scale pathways with the multi-kernel perception mapping module. This synergistic approach achieves consistent enhancement of cross-domain features. Experimental results on the public datasets NEU-DET and GC10-DET demonstrate that CDCM-DETR achieves the mAP50 improvement of 4.6% and 4.3%, respectively, over the baseline model RT-DETR, while reducing the number of parameters by approximately 37.4%. This fully validates the method’s effectiveness in complex industrial scenarios.