Defect Detection

Defect detection research focuses on automatically identifying flaws in various manufactured products and infrastructure components, aiming to improve efficiency and quality control. Current efforts leverage deep learning, employing architectures like convolutional neural networks (CNNs), vision transformers (ViTs), and generative adversarial networks (GANs), often within ensemble learning frameworks, to achieve high accuracy and real-time performance. These advancements are crucial for diverse applications, including industrial inspection (e.g., PCBs, solar panels, metal surfaces), infrastructure monitoring (e.g., powerlines, sewer systems, wind turbines), and even code quality assessment, ultimately leading to improved product quality, reduced costs, and enhanced safety.