Surface Anomaly Detection

Surface anomaly detection focuses on identifying deviations from expected patterns in surface data, whether from images, point clouds, or tactile sensors, across diverse applications like manufacturing inspection and medical imaging. Current research emphasizes the development of robust algorithms, including deep learning models (e.g., autoencoders, diffusion models, and contrastive learning approaches), often leveraging pre-trained networks adapted for specific domains to improve accuracy and efficiency. These advancements are crucial for improving quality control in manufacturing, enabling early detection of defects in infrastructure, and facilitating advancements in medical diagnostics.