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.

翻译：随着深度学习技术的快速发展，计算机视觉在零售自动化领域展现出巨大潜力。本文提出了一种基于改进YOLOv10网络的新型零售自助结账系统，旨在提升结账效率并降低人力成本。我们通过对YOLOv10模型进行针对性优化，引入YOLOv8的检测头结构，显著提高了商品识别准确率。此外，我们开发了专为自助结账场景设计的后处理算法，以进一步增强系统的实际应用性能。实验结果表明，我们的系统在商品识别准确率和结账速度方面均优于现有方法。本研究不仅为零售自动化提供了新的技术解决方案，也为优化深度学习模型在实际应用中的性能提供了有价值的见解。