Title: A multi-attribute recognition method of vehicle's line-pressing in parking lot based on multi-task convolution neural network

Authors: Shaohui Zhong; Ting Hu

Addresses: School of Information, Hunan Radio and TV University, Changsha 410004, China ' School of Information, Hunan Radio and TV University, Changsha 410004, China

Abstract: In order to solve the problems of low recognition accuracy and long recognition time, a multi-attribute recognition method based on multi-task convolution neural network is proposed. The structure principle of multi-task convolution neural network is analysed, and multi-task is set in the bottom area of convolutional neural network. The Hough transform is used to extract the parking line in the parking lot, and the input layer of the multi-attribute label structure is established by multi-attribute classification convolution neural network. The loss function of vehicle line pressing attributes in different parking lots is obtained by combining the full connection layer and the connecting sub layer. The multi-attribute recognition of vehicle pressure line is realised by measuring and learning the line voltage attributes of vehicles. The experimental results show that the method can effectively identify the line pressing situation of vehicles in parking lot, and the recognition accuracy can reach 99%.

Keywords: multi-task; convolution neural network; parking lot; line-pressing of vehicle; multi-attribute; recognition method.

DOI: 10.1504/IJICT.2022.121789

International Journal of Information and Communication Technology, 2022 Vol.20 No.3, pp.308 - 324

Received: 24 Jun 2020
Accepted: 13 Sep 2020
Published online: 07 Apr 2022 *

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