Visual Anomaly Detection

Visual anomaly detection is a computer vision technique that enables systems to identify deviations from the norm by learning the typical appearance of objects or scenes and flagging unusual patterns. This process is crucial in environments where unseen anomalies pose significant risks. The method involves generating anomaly scores and maps to pinpoint deviations, relying heavily on a well-curated dataset that captures various normal variations under real conditions. Different types of anomalies, such as structural, logical, textural, and semantic, require distinct detection methods ranging from fully supervised to zero-shot approaches. Roboflow provides tools to build anomaly detection workflows, offering supervised and unsupervised pathways to cater to known defects or novel discoveries. These systems are vital for real-time monitoring and inspection across industries, leveraging advanced techniques like memory-bank methods, distribution modeling, and vision-language models to enhance detection accuracy and adaptability.