Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Abstract

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest; (2) tracking objects in consecutive frames; and (3) counting objects using a track management pipeline. Several data generation techniques—including copy-and-paste, random placement, and augmentation—are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792.