Enhanced Self-Checkout System for Retail Based on Improved YOLOv10
This work addresses retail automation to reduce labor costs, but it appears incremental as it modifies an existing model for a specific application.
This paper tackles the problem of retail checkout automation by developing a self-checkout system based on an improved YOLOv10 network, resulting in enhanced product recognition accuracy and checkout speed compared to existing methods.
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.