Classification Network for Surface Defects in Steel Strips Based on Enhanced MobileNetV2

This paper proposes a steel strip surface defect detection method based on improved MobileNetV2 and semi-supervised learning. Addressing the issues of high computational complexity and insufficient few-shot learning capability in traditional steel strip defect detection approaches, this study introduces Squeeze-and-Excitation (SE) attention mechanisms and a multi-scale Feature Pyramid Network (FPN) to enhance MobileNetV2 architecture. Combined with a consistency regularization-based semi-supervised learning strategy, the proposed method significantly improves detection performance under limited annotated data conditions. Experimental results demonstrate that the approach achieves 97.3% mean accuracy on steel strip surface defect datasets while maintaining high computational efficiency, providing a feasible solution for real-time defect detection in industrial scenarios.