论文标题
一种基于区域的深度学习方法，用于自动零售结帐
A Region-Based Deep Learning Approach to Automated Retail Checkout
论文作者
论文摘要
在常规零售商店中自动化产品结帐流程是一项任务，该任务旨在对社会一般影响产生巨大影响。为此，可靠的深度学习模型可以使自动化产品进行快速客户结帐，这可以使这个目标成为现实。在这项工作中，我们提出了一种基于新型的，基于区域的深度学习方法，以使用自定义的Yolov5对象检测管道和DeepSort算法自动化产品计数。我们在具有挑战性的现实世界测试视频方面的结果表明，我们的方法可以将其预测推广到足够的准确性水平，并且具有足够快的运行时，可以保证部署到现实世界中的商业环境。我们提出的方法在2022年AI City Challenge（轨道4）中获得第四名，实验验证数据的F1得分为0.4400。
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of accuracy and with a fast enough runtime to warrant deployment to real-world commercial settings. Our proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an F1 score of 0.4400 on experimental validation data.
