An Elevator Button Recognition Method Combining YOLOv5 and OCR  

作　　者：Xinliang Tang Caixing Wang Jingfang Su Cecilia Taylor 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第4期117-131,共15页计算机、材料和连续体（英文）

基　　金：the Research and Implementation of An Intelligent Driving Assistance System Based on Augmented Reality in Hebei Science and Technology Support Plan (Grant Number 17210803D);Science and Technology Research Project of Higher Education in Hebei Province (Grant Number ZD2020318);Middle School Students Science and Technology Innovation Ability Cultivation Special Project (Grant No.22E50075D)and project (Grant No.1181480).

摘　　要：Fast recognition of elevator buttons is a key step for service robots toride elevators automatically. Although there are some studies in this field, noneof them can achieve real-time application due to problems such as recognitionspeed and algorithm complexity. Elevator button recognition is a comprehensiveproblem. Not only does it need to detect the position of multiple buttonsat the same time, but also needs to accurately identify the characters on eachbutton. The latest version 5 of you only look once algorithm (YOLOv5) hasthe fastest reasoning speed and can be used for detecting multiple objects inreal-time. The advantages ofYOLOv5 make it an ideal choice for detecting theposition of multiple buttons in an elevator, but it’s not good at specific wordrecognition. Optical character recognition (OCR) is a well-known techniquefor character recognition. This paper innovatively improved the YOLOv5network, integrated OCR technology, and applied them to the elevator buttonrecognition process. First, we changed the detection scale in the YOLOv5network and only maintained the detection scales of 40 ∗ 40 and 80 ∗ 80, thusimproving the overall object detection speed. Then, we put a modified OCRbranch after the YOLOv5 network to identify the numbers on the buttons.Finally, we verified this method on different datasets and compared it withother typical methods. The results show that the average recall and precisionof this method are 81.2% and 92.4%. Compared with others, the accuracyof this method has reached a very high level, but the recognition speed hasreached 0.056 s, which is far higher than other methods.

关 键 词：Button recognition deep learning multi-object detection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]