Multi-scale contextual modeling and fine-grained adaptive fusion for real-time surface defect detection

Abstract

Abstract Surface defect detection in steel strips requires both accurate recognition and efficient inference under complex industrial backgrounds. Existing methods still encounter difficulties in modeling scale-varied defects, preserving fine-grained defect cues, and aligning multi-level features efficiently. To address these issues, we propose CDF-YOLO, a real-time detection framework that integrates multi-scale contextual modeling and fine-grained adaptive fusion on the YOLOv12 baseline. Specifically, a Dilated Context Pyramid Bottleneck (C2f_DCPB) is introduced to enhance contextual representation through parallel dilated branches; a Fine-grained Adaptive Fusion module (FAN_Block) is embedded into the high-resolution path to strengthen local texture and structural-detail representation; and DySample is adopted to improve content-adaptive upsampling for multi-scale feature fusion. Experiments on the NEU-DET dataset show that CDF-YOLO achieves an mAP50 of 95.4% and 101.3 FPS under the adopted test protocol, while cross-dataset evaluation on DeepPCB further indicates improved generalization performance. These results suggest that CDF-YOLO provides a favorable accuracy-efficiency trade-off on public industrial defect datasets. Nevertheless, validation on larger-scale production-line data and further optimization for edge deployment remain necessary for practical industrial application.