# A Region-Based Deep Learning Approach to Automated Retail Checkout

arXiv: https://arxiv.org/abs/2204.08584 · HTML: https://mlsanity.com/paper/2204.08584/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [Deep Affinity Network for Multiple Object Tracking](https://mlsanity.com/paper/1810.11780/) (2018)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond](https://mlsanity.com/paper/2202.14034/) (2022)
- [Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles](https://mlsanity.com/paper/2111.09496/) (2021)
- [ARC: A Vision-based Automatic Retail Checkout System](https://mlsanity.com/paper/2104.02832/) (2021)

## Built on by (most cited first, 9 of 9)

- [The 6th AI City Challenge](https://mlsanity.com/paper/2204.10380/) (2022)
- [Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8](https://mlsanity.com/paper/2304.08256/) (2023)
- [Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis](https://mlsanity.com/paper/2404.08229/) (2024)
- [Enhanced Self-Checkout System for Retail Based on Improved YOLOv10](https://mlsanity.com/paper/2407.21308/) (2024)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)
- [Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets](https://mlsanity.com/paper/2404.10078/) (2024)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)
- [SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos](https://mlsanity.com/paper/2304.09247/) (2023)
- [State of play and future directions in industrial computer vision AI standards](https://mlsanity.com/paper/2503.02675/) (2025)

## Abstract (arXiv)

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.