Tan Lianghao, Liu Shubing, Gao Jing, Liu Xiaoyi, Chu Linyue, Jiang Huangqi
Department of Information System, Arizona State University, Tempe, AZ 85281, USA.
Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
J Imaging. 2024 Oct 10;10(10):248. doi: 10.3390/jimaging10100248.
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 for the YOLOv10 model, 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 the 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.
随着深度学习技术的迅速发展，计算机视觉在零售自动化中展现出了巨大潜力。本文提出了一种基于改进的YOLOv10网络的新型零售自助收银系统，旨在提高收银效率并降低劳动力成本。我们针对YOLOv10模型提出了有针对性的优化措施，融入了YOLOv8的检测头结构，显著提高了产品识别准确率。此外，我们还开发了一种针对自助收银场景量身定制的后处理算法，以进一步增强系统的应用效果。实验结果表明，我们的系统在产品识别准确率和收银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一种新的技术解决方案，还为优化深度学习模型以用于实际应用提供了有价值的见解。
