Texture Anomaly

Texture anomaly detection focuses on identifying deviations from expected patterns in textured surfaces, crucial for quality control in manufacturing and other industrial processes. Current research emphasizes unsupervised learning methods, employing architectures like autoencoders, generative adversarial networks (GANs), and knowledge distillation techniques, often aiming for zero-shot or few-shot learning capabilities to reduce the need for extensive training data. These advancements are improving the speed and accuracy of defect detection in diverse applications, ranging from fabric inspection to semiconductor wafer analysis, leading to increased efficiency and reduced production costs. The development of robust benchmark datasets is also a key area of focus, enabling more rigorous evaluation and comparison of different anomaly detection algorithms.