SCL—Segmentation–Classification combined Loss for surface defect detection

Deep learning for surface defect detection has recently received increased attention in relation to quality control of industrial products. In this paper, we present a combined loss function called Segmentation–Classification combined Loss (SCL). It exploits the binary information (normal/anomalous sample) implicit in the segmentation masks to reduce false positives and false negatives in surface defect detection tasks. We evaluate the method on images in three different domains: defects in electrical commutators, defects in a weakly labeled synthetic dataset, and defects of glass bottles. Our combined loss function outperforms the state of the art in the Kolektor (electrical commutators) dataset and significantly improves the classical Dice segmentation loss function in terms of both average precision, false positives, and false negatives. Moreover, the combined loss function introduced in this paper can be applied in a straightforward manner to any loss-based segmentation model. Finally, in this work we introduce and publish a new dataset for the still poorly explored task of surface defect detection of glass bottles.