Anomaly Detection and Segmentation for Surface Inspection
What is Anomaly Detection:
Anomaly detection is the identification of items that do not conform to an expected pattern or to other items in a dataset that can only be detected by well-trained experts. Such anomalies can be translated into problems such as structural and surface inspection, medical image diagnosis.
Why it is important?
Quality of goods has become increasingly important to save natural resources and costs. Modern businesses are beginning to understand the importance of manufacturing and delivering quality products. Quality checks and surface inspection performed by human experts are highly time-consuming, costly and less effective to detect defects or anomalies. Not only in the manufacturing industry but also in the medical domain, we face the same problem due to scarce expert know-how.
Anomaly detection is key for solving such tasks automatically.
Artificial Intelligence for Anomaly detection and segmentation:
One of the major concerns of research groups and companies working on anomaly detection tasks in computer vision is to obtain large amounts of labeled training data and to deal with the problem of imbalanced data. As a result, many supervised and semi-supervised approaches do not perform well.
To deal with such a crucial issue, our team at MoonVision proposes a very effective solution using one-class supervised learning. Through an image completion approach comparatively little and much cheaper to obtain data is required. The method achieves state of the art in discovering structural anomalies.
Our Approach:
We used a one-class unsupervised learning image completion approach on fault-free samples by training a deep convolutional neural network ( an autoencoder and decoder) to complete images in which some regions are cut out. The network was trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region. The pixel-wise reconstruction error within the cut-out region is an anomaly image that is used to score the degree of anomal regions. Results on surface images of, e.g. painted or milled surface areas demonstrate that this approach is suitable for the detection of visible anomalies and moreover surpasses all other tested methods.
Results and Inference:
Conclusion
Our one-class unsupervised learning image completion approach is the state of the art approach for solving anomaly detection and segmentation tasks in artificial intelligence which require a small amount of data.
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