Open Access

ARTICLE

An Elevator Button Recognition Method Combining YOLOv5 and OCR

by Xinliang Tang¹, Caixing Wang¹, Jingfang Su^1,*, Cecilia Taylor²

1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China
2 Nankai-Birmingham Institute of Data Science Intelligence, Birmingham, B100AB, Britain

* Corresponding Author: Jingfang Su. Email:

Computers, Materials & Continua 2023, 75(1), 117-131. https://doi.org/10.32604/cmc.2023.033327

Received 14 June 2022; Accepted 15 November 2022; Issue published 06 February 2023

Abstract

Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in real-time. The advantages of YOLOv5 make it an ideal choice for detecting the position of multiple buttons in an elevator, but it’s not good at specific word recognition. Optical character recognition (OCR) is a well-known technique for character recognition. This paper innovatively improved the YOLOv5 network, integrated OCR technology, and applied them to the elevator button recognition process. First, we changed the detection scale in the YOLOv5 network and only maintained the detection scales of 40 * 40 and 80 * 80, thus improving the overall object detection speed. Then, we put a modified OCR branch after the YOLOv5 network to identify the numbers on the buttons. Finally, we verified this method on different datasets and compared it with other typical methods. The results show that the average recall and precision of this method are 81.2% and 92.4%. Compared with others, the accuracy of this method has reached a very high level, but the recognition speed has reached 0.056 s, which is far higher than other methods.

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Keywords

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