Steel Surface Defect

Steel surface defect detection is crucial for ensuring product quality and safety across various industries. Current research heavily emphasizes automated visual inspection using deep learning, with convolutional neural networks (CNNs), particularly those incorporating transfer learning and transformer architectures, showing high accuracy in classifying and segmenting defects. These advanced models address challenges like data scarcity through techniques such as data augmentation and few-shot learning, aiming for real-time performance and robustness against varying lighting and noise conditions. The development of efficient and accurate automated defect detection systems promises significant improvements in manufacturing efficiency and reduced production costs.