Anomaly region detection and localization in metal surface inspection
Visual inspection and identification of anomalies are important steps in the manufacturing process. A data set containing images of metallic components at different orientations and lighting conditions have been used for this experiment. Our aim is to find the anomaly in an image by subtracting it from an equivalent reference image with no anomalies. This is challenging due to the availability of multiple and slightly changing viewpoints for the reference image. The anomalies can only be detected in the residue obtained by using an identical reference image with no anomalies in the subtraction process. The proposed system is based on finding the best reference from a pool of similar images. A method for ranking the images based on similarity is also introduced. The proposed method scales well for high contrast images with dark colored anomalies.