Surface Defect

Surface defect detection focuses on automatically identifying imperfections on various materials' surfaces, aiming to improve efficiency and quality control in manufacturing and other industries. Current research heavily utilizes deep learning, employing architectures like Vision Transformers, YOLOv5, and convolutional neural networks (CNNs) coupled with techniques such as attention mechanisms and feature fusion to enhance accuracy and speed, even with limited or noisy data. These advancements are crucial for optimizing industrial processes, reducing costs associated with defective products, and enabling real-time monitoring in applications ranging from metal manufacturing and renewable energy asset inspection to medical imaging.