Industrial Image Anomaly Detection

Industrial image anomaly detection (IAD) focuses on automatically identifying defects or deviations from expected patterns in manufacturing images, improving quality control and reducing costs. Current research emphasizes developing robust models capable of handling multi-class anomalies and limited training data, employing techniques like transformer networks, self-supervised learning, and generative models to learn normal patterns and effectively detect deviations. These advancements are crucial for enhancing industrial processes, enabling faster and more accurate defect detection, and ultimately leading to improved product quality and efficiency.