Amazon Lookout for Vision provides a machine learning (ML)-based anomaly detection service to identify normal images (i.e., images of objects without defects) vs anomalous images (i.e., images of objects with defects), types of anomalies (e.g., missing piece), and the location of these anomalies. Therefore, Lookout for Vision is popular among customers that look for automated solutions for industrial quality inspection (e.g., detecting abnormal products). However, customers’ datasets usually face two problems:

The number of images with anomalies could be very low and might not reach anomalies/defect type minimum imposed by Lookout for Vision (~20).
Normal images might not have enough diversity and might result in the model failing when environmental conditions such as lighting change in production

To overcome these problems, this post introduces an image augmentation pipeline that targets both problems: It provides a way to generate synthetic anomalous images by removing objects in images and

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