A Region-Based Deep Learning Approach to Automated Retail Checkout
This addresses the problem of automating checkout processes for retail stores, but it is incremental as it builds on existing object detection and tracking methods.
The paper tackled automated product counting for retail checkout by proposing a region-based deep learning approach, achieving an F1 score of 0.4400 and winning 4th place in the 2022 AI City Challenge.
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.