Title: Detection and recognition of text traffic signs above the road

Authors: Wei Sun; Yangtao Du; Xu Zhang; Guoce Zhang

Addresses: Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, Jiangsu, China ' School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China ' School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China ' School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China

Abstract: Based on the similarity between traffic sign images in the source and target domains, the parameters migrated from the source domain are utilised as the initial parameters of the faster region convolutional neural network (Faster-R-CNN), which is trained for detecting text traffic signs, and then fine-tune the network parameters based on the samples in target domain for obtaining the final network parameters. Moreover, the converted traffic sign images from RGB to HSV space is also used as the training samples of the network, thereby overcoming the under-learning problem of model caused by less training samples. The traditional efficient and accurate scene text (EAST) detection network model is tailored and a new recognition model is proposed based on the extreme learning machine (ELM) classifier to identify and classify the detected text traffic signs above the road. Experimental results in the natural scene demonstrate the effectiveness of the proposed method.

Keywords: text traffic signs; computer vision; convolutional neural network; text detection; transfer learning; extreme learning machine classifier; data augmentation.

DOI: 10.1504/IJSNET.2021.113626

International Journal of Sensor Networks, 2021 Vol.35 No.2, pp.69 - 78

Received: 23 Mar 2020
Accepted: 27 May 2020

Published online: 09 Mar 2021 *

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