Title: An image recognition method for speed limit plate based on deep learning algorithm

Authors: Jian Gao

Addresses: Research Institute of Highway Ministry of Transport, Haidian, Beijing 100088, China

Abstract: In order to overcome the problems of large number of sample data collected from speed limit image and unclear image feature hierarchy, a recognition method for speed limit plate image based on deep learning algorithm is proposed. This method combines deep learning with SVM to build a multi-level classification model, and uses deep learning method to re-represent the original data through unsupervised learning. The image features of speed limit plate are extracted in depth, and the image is preprocessed by colour component compensation, image denoising and threshold segmentation. The similarity between the image features extracted layer by layer and the standard features is calculated, and the final recognition result of speed limit plate image is obtained through feature matching. The experimental results show that the average recognition rate is increased by 6.6%, which can effectively provide data reference for vehicle speed control in the actual driving process.

Keywords: deep learning algorithm; speed limit plate; image recognition; SVM.

DOI: 10.1504/IJICT.2022.120638

International Journal of Information and Communication Technology, 2022 Vol.20 No.2, pp.216 - 230

Received: 28 Apr 2020
Accepted: 15 Aug 2020
Published online: 31 Jan 2022 *

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