论文标题
迈向计算机视觉技术：自动效用仪的半自动读数
Towards computer vision technologies: Semi-automated reading of automated utility meters
论文作者
论文摘要
在本报告中，我们分析了使用计算机视觉技术来自动读取实用程序仪表的可能性。在我们的研究中，我们专注于两种计算机视觉技术：开源解决方案张量对象检测（TensorFlow）和一个商业解决方案。该报告扩展了我们以前的出版物：我们首先介绍相关方法的结构化分析。之后，我们提供了两种计算机视觉技术，TensorFlow对象检测（TensorFlow）和Anyline的详细比较，该比较应用于实用程序仪表的半自动读取。在本文中，我们讨论了应用于公用事业表阅读的每种解决方案的局限性和好处，尤其是专注于准确性和推理时间等方面。我们的目标是确定最适合该特定应用领域的解决方案，那里存在一些特定的挑战。
In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. This report extends our previous publication: We start with presentation of a structured analysis of related approaches. After that we provide a detailed comparison of two computer vision technologies, Tensorflow Object Detection (Tensorflow) and Anyline, applied to semi-automated reading of utility meters. In this paper, we discuss limitations and benefits of each solution applied to utility meters reading, especially focusing on aspects such as accuracy and inference time. Our goal was to determine the solution that is the most suitable for this particular application area, where there are several specific challenges.
