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Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering

: Lehr, J.; Sargsyan, A.; Pape, M.; Philipps, J.; Krüger, J.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020. Proceedings : Vienna, Austria - Hybrid, 08 - 11 September 2020
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-8956-7
ISBN: 978-1-7281-8957-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <25, 2020, Online>
Conference Paper
Fraunhofer IPK ()

Neural networks have proven to be extraordinarily successful in many computer vision applications. But the approaches used to train neural networks require large datasets of annotated images, which requires a solid amount of human time to prepare those datasets. To facilitate the adoption of machine learning based technologies in industrial computer vision applications, this paper presents a two-step unsupervised learning approach for anomaly detection with further defect clusterization. In the first stage, the defects are not explicitly learned, but are interpreted as an anomaly or novelty based on the dataset of defect-free samples. In a second stage, the anomalies detected in the first stage are clustered in unsupervised manner and classified into meaningful categories by experts with process knowledge (e.g. critical or non-critical defect). This paper presents a first small dataset containing one industrial object with a complex shape. The object is made of aluminiu m and is shown both free of defects and defective. Based on this, recommendations are given for an acquisition setup for a large, extensive dataset. Most of the existing papers are studying the approaches for uniform surface (texture) inspection. The specifics of this research is to identify defects on rigid bodies, which exhibit highly non uniform texture in the image. State of the art methods were evaluated and improved to increase the classification accuracy. With a fine-tuned ResNet-18 it was possible to achieve 100% accuracy for defective and defect-free images.