Industrial Defect

Industrial defect detection aims to automate the identification of flaws in manufactured products, improving quality control and efficiency across various industries. Current research heavily utilizes deep learning, employing models like convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid architectures, often incorporating techniques like self-supervised learning and few-shot learning to address data scarcity and variability. These advancements are crucial for enhancing product quality, reducing waste, and improving the safety and reliability of manufactured goods, particularly in sectors like manufacturing, energy production, and food processing. The development and evaluation of robust, efficient, and generalizable models are key focuses, often relying on publicly available benchmark datasets for comparison and validation.