FMV-YOLO: A Steel Surface Defect Detection Algorithm for Real-World Scenarios

Abstract

Surface defects during steel production can severely impact product quality and safety, making defect detection crucial. To improve the precision and performance of conventional approaches, we introduce FMV-YOLO, a model for detecting steel surface defects, built upon YOLOv11n. First, we substitute the C2PSA attention module in the backbone network with an Adaptive Fine-Grained Channel Attention (FCA) module, which improves defect type identification while reducing the parameter count. Next, we incorporate a new Multi-Scale Attention Fusion module (MSAF) to strengthen feature representation and refine the loss function using Normalized Wasserstein Distance (NWD) loss, thereby improving the localization accuracy of small defects. Finally, we integrate the VoV-GSCSP module within the neck network to achieve lightweighting, facilitating real-world deployment. Extensive experiments on the GC10DET and NEU-DET datasets demonstrate that the model effectively balances detection accuracy, parameter count, and computational load. With 2.6M parameters and 5.7G FLOPs, the model attains an [email protected] of 73.4% on GC10DET and 80.2% on NEU-DET. Additionally, the method achieves 99% detection accuracy on a self-constructed industrial dataset, proving its effectiveness in industrial defect detection.