Enhanced Self-Checkout System for Retail Based on Improved YOLOv10
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910567336247296 |
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| author | Tan, Lianghao Liu, Shubing Gao, Jing Liu, Xiaoyi Chu, Linyue Jiang, Huangqi |
| author_facet | Tan, Lianghao Liu, Shubing Gao, Jing Liu, Xiaoyi Chu, Linyue Jiang, Huangqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced Self-Checkout System for Retail Based on Improved YOLOv10 Tan, Lianghao Liu, Shubing Gao, Jing Liu, Xiaoyi Chu, Linyue Jiang, Huangqi Computer Vision and Pattern Recognition 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. |
| title | Enhanced Self-Checkout System for Retail Based on Improved YOLOv10 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.21308 |