Self-Checkout Product Detection with Occlusion Layer Prediction and Intersection Weighting

Automatic self-checkout based on computer vision is gaining popularity in the field of retail industry, due to the convenience for customers and manpower saving. Thus, retail product detection is vital important in the process of automatic checkout. The task of product detection based on single camera is still challenging, like (1) holding a variety of different products in one or both hands, (2) variable product appearance, (3) intentional fraudulent checkout practices. In this paper, we introduce a third branch on ordinary detectors to predict the occlusion layer of a product and then adopt occlusion layer aware non-max suppression (OLA-NMS) to depress false positives while keeping detection rate. Furthermore, IoU-activate loss is adopted by considering location information in the classification loss. Our third contribution is that we have collected a large-scale of retail checkout images for the target of self-checkout monitoring (SCOM), since there is no dataset or benchmark available for retail product detection under occlusion. Experiments are conducted on SCOM dataset to demonstrate the effectiveness of the proposed method.