mlsanityData to 14 Oct 2026

Enhanced Self-Checkout System for Retail Based on Improved YOLOv10

arXiv 2407.21308 ↗First posted 31 Jul 2024cs.CVcited by 7

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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.

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

22 of 49 references.

References · 22

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  3. 2015Fast R-CNNcited by 2,933
  4. 2018YOLOv3: An Incremental Improvementcited by 2,659
  5. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  6. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
  7. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  8. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  9. 2015Recent Advances in Convolutional Neural Networkscited by 360
  10. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
  11. 2021TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarioscited by 83
  12. 2017FSSD: Feature Fusion Single Shot Multibox Detectorcited by 43
10 more
  1. 2023Efficient Multi-Scale Attention Module with Cross-Spatial Learningcited by 41
  2. 2019Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathologycited by 36
  3. 2019RPC: A Large-Scale Retail Product Checkout Datasetcited by 27
  4. 2024LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Networkcited by 27
  5. 2022MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularizationcited by 22
  6. 2024Real-Time Pill Identification for the Visually Impaired Using Deep Learningcited by 21
  7. 2024Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimizationcited by 12
  8. 2024YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Visioncited by 12
  9. 2022A Region-Based Deep Learning Approach to Automated Retail Checkoutcited by 9
  10. 2024GraphCNNpred: A stock market indices prediction using a Graph based deep learning systemcited by 3

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Cited by year

Citing papers per year: 2024: 7.

7 papers.

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  3. 2024Comprehensive Overview of Artificial Intelligence Applications in Modern Industriescited by 6
  4. 2024Enhanced Credit Score Prediction Using Ensemble Deep Learning Modelcited by 2
  5. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  6. 2024Mitigating Knowledge Conflicts in Language Model-Driven Question Answeringcited by 1
  7. 2024Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challengescited by 0