# Enhanced Self-Checkout System for Retail Based on Improved YOLOv10

arXiv: https://arxiv.org/abs/2407.21308 · HTML: https://mlsanity.com/paper/2407.21308/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (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)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)
- [FSSD: Feature Fusion Single Shot Multibox Detector](https://mlsanity.com/paper/1712.00960/) (2017)
- [Efficient Multi-Scale Attention Module with Cross-Spatial Learning](https://mlsanity.com/paper/2305.13563/) (2023)
- [Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology](https://mlsanity.com/paper/1904.06040/) (2019)
- [RPC: A Large-Scale Retail Product Checkout Dataset](https://mlsanity.com/paper/1901.07249/) (2019)
- [LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network](https://mlsanity.com/paper/2404.01614/) (2024)
- [MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization](https://mlsanity.com/paper/2203.14316/) (2022)
- [Real-Time Pill Identification for the Visually Impaired Using Deep Learning](https://mlsanity.com/paper/2405.05983/) (2024)
- [Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization](https://mlsanity.com/paper/2403.13703/) (2024)
- [YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision](https://mlsanity.com/paper/2407.02988/) (2024)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [GraphCNNpred: A stock market indices prediction using a Graph based deep learning system](https://mlsanity.com/paper/2407.03760/) (2024)

## Built on by (most cited first, 7 of 7)

- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way](https://mlsanity.com/paper/2409.14577/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Mitigating Knowledge Conflicts in Language Model-Driven Question Answering](https://mlsanity.com/paper/2411.11344/) (2024)
- [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024)

## Abstract (arXiv)

With the rapid advancement of deep learning technologies, computer vision has shown immense potential in retail automation. This paper presents a novel self-checkout system for retail based on an improved YOLOv10 network, aimed at enhancing checkout efficiency and reducing labor costs. We propose targeted optimizations to the YOLOv10 model, by incorporating the detection head structure from YOLOv8, which significantly improves product recognition accuracy. Additionally, we develop a post-processing algorithm tailored for self-checkout scenarios, to further enhance the application of system. Experimental results demonstrate that our system outperforms existing methods in both product recognition accuracy and checkout speed. This research not only provides a new technical solution for retail automation but offers valuable insights into optimizing deep learning models for real-world applications.