Decoupled Edge Guidance Network for Automatic Checkout.

Abstract

Automatic checkout (ACO) aims at correctly generating complete shopping lists from checkout images. However, the domain gap between the single product in training data and multiple products in checkout images endows ACO tasks with a major difficulty. Despite remarkable advancements in recent years, resolving the significant domain gap remains challenging. It is possibly because networks trained solely on synthesized images may struggle to generalize well to realistic checkout scenarios. To this end, we propose a decoupled edge guidance network (DEGNet), which integrates synthesized and checkout images via a supervised domain adaptation approach and further learns common domain representations using a domain adapter. Specifically, an edge embedding module is designed for generating edge embedding images to introduce edge information. On this basis, we develop a decoupled feature extractor that takes original images and edge embedding images as input to jointly utilize image information and edge information. Furthermore, a novel proposal divide-and-conquer strategy (PDS) is proposed for the purpose of augmenting high-quality samples. Through experimental evaluation, DEGNet achieves state-of-the-art performance on the retail product checkout (RPC) dataset, with checkout accuracy (cAcc) results of 93.47% and 95.25% in the average mode of faster RCNN and cascade RCNN frameworks, respectively. Codes are available at https://github.com/yourbikun/DEGNet.