Researchers are exploring various methods for detecting metal surface defects, including the use of dynamic fine-grained multi-branch encoders and other deep learning-based approaches. Different techniques, such as YOLO algorithms and contrastive learning, are being investigated for their effectiveness in detecting defects. The development of efficient and lightweight algorithms for real-time defect detection is also a focus of the research.