Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of accuracy and with a fast enough runtime to warrant deployment to real-world commercial settings. Our proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an F1 score of 0.4400 on experimental validation data.
@article{arxiv.2204.08584,
title = {A Region-Based Deep Learning Approach to Automated Retail Checkout},
author = {Maged Shoman and Armstrong Aboah and Alex Morehead and Ye Duan and Abdulateef Daud and Yaw Adu-Gyamfi},
journal= {arXiv preprint arXiv:2204.08584},
year = {2022}
}