WaveDet: Wavelet-Based adaptive defect detection with multi-resolution feature analysis for steel surfaces

WAD-YOLO (Wavelet-Based Adaptive Defect Detection with Multi-Resolution Feature Analysis) is an intelligent system for automatic steel surface defect detection. The project combines wavelet transform techniques with the YOLO deep learning model for accurate and real-time inspection. Wavelet decomposition extracts multi-resolution features to highlight defects of different sizes and textures. Adaptive enhancement improves image clarity and reduces noise effects. The processed features are fed into the YOLO network for defect localization and classification. The system detects various defects such as cracks, scratches, pits, and rolled-in scale. Multi-resolution analysis ensures better detection of both small and large surface defects. The model improves detection accuracy compared to traditional single-scale methods. It supports real-time industrial inspection on production lines. Overall, WAD-YOLO enhances quality control efficiency in steel manufacturing industries.