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Enhanced Self-Checkout System for Retail Based on Improved YOLOv10
Lianghao Tan1, Shubing Liu2, Jing Gao3
1Department of Information System, Arizona State University, Tempe, AZ 85281, USA.
Journal of Imaging
|October 25, 2024
Summary
This study introduces an improved YOLOv10 deep learning model for retail self-checkout systems. The enhanced system boosts product recognition accuracy and checkout speed, reducing labor costs in retail automation.
Area of Science:
- Computer Vision
- Deep Learning
- Retail Automation
Background:
- Deep learning and computer vision offer significant potential for automating retail operations.
- Current self-checkout systems face challenges in efficiency and accuracy.
- Labor costs are a major concern for the retail industry.
Purpose of the Study:
- To develop an advanced self-checkout system for retail environments.
- To improve product recognition accuracy and checkout speed.
- To reduce operational costs through enhanced automation.
Main Methods:
- An improved YOLOv10 network was utilized for product detection.
- The YOLOv10 model was optimized with the YOLOv8 detection head structure.
- A specialized post-processing algorithm was developed for self-checkout scenarios.
Main Results:
- The proposed system demonstrated superior product recognition accuracy compared to existing methods.
- Experimental results confirmed enhanced checkout speed.
- The optimized model and algorithm significantly improved system performance.
Conclusions:
- The novel self-checkout system offers a viable technical solution for retail automation.
- Optimized deep learning models can be effectively applied to real-world retail challenges.
- The research contributes to increasing checkout efficiency and reducing labor costs.
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