Surface Defect Detection

Surface defect detection aims to automatically identify and classify imperfections on surfaces, crucial for quality control in various industries. Current research emphasizes improving the robustness and efficiency of detection methods, focusing on deep learning architectures like convolutional neural networks (CNNs), transformers, and autoencoders, often incorporating techniques such as federated learning and data augmentation to address data scarcity and heterogeneity. These advancements are significant for enhancing manufacturing processes, improving product quality, and reducing inspection costs across diverse sectors, from electronics to materials science.