深度学习数字仪表盘识别_基于深度学习的指针式仪表自动识别的研究和设计

Automatic Recognition of Dial Instrument Based on Deep Learning

HE Jiaqi

1

贺嘉琪(1992-)，男，硕士研究生，主要研究方向：计算机视觉、图像识别、机器学习

XIONG Yongping

1

熊永平(1982-)，男，副教授，博导，主要研究方向：自然语言处理、图像识别、机器学习；

WU Guibin

1

伍贵宾(1980-)，男，博士，主要研究方向：数据挖掘、图像识别；

1、Institute of Network Technology, Beijing University of Posts and Telecommunications,Beijing 100876

Abstract：With the continuous development of technology and the continuous improvement of industrial informationization and digitization, it is especially important to carry out efficient and accurate data entry for traditional pointer instruments in industrial production. Aiming at the problem of the demanding environment and the single type of instrument recognition in the current automatic recognition system, this paper combines the related techniques of deep learning and computer vision to research and improve the existing process identification algorithm.By constructing the instrument training data set and learning and adjusting the target detection model MASKRCNN to realize the image segmentation and effective information extraction of the instrument panel in the natural scene.According to the design of image feature extraction, this paper use the Ostu threshold segmentation method and KNN to identify the instrument numbers, use the probabilistic Hough line method to fit and locate the pointer, and use the distance method to determine the indication at last in order to improving the robustness and generalization of the pointer type automatic identification system.