Cutout as augmentation in contrastive learning for detecting burn marks in plastic granules

Abstract

Abstract. Plastic granules are a common delivery form for creating products in industries such as the plastic manufacturing, construction and automotive ones. In the corresponding sorting process of plastic granules, diverse defect types could appear. Burn marks, which potentially lead to weakened structural integrity of the plastic, are one of the most common types. Thus, plastic granules with burn marks should be filtered out during the sorting process. Artificial intelligence (AI)-based anomaly detection approaches are widely used in the field of visual-based sorting due to the higher accuracy and lower requirement of expert knowledge compared with classic rule-based algorithms (Chandola et al., 2009). In this contribution, a simple data augmentation strategy, cutout, is implemented as a way of simulating defects when combined with a contrastive learning-based methodology and is proven to improve the accuracy of the anomaly detection of burn marks. Different variants of cutout are also evaluated. Specifically, synthetic image data are used due to the lack of real data.