基于改进级联卷积神经网络的交通标志识别*

doi:10.19562/j.chinasae.qcgc.2020.09.016

Abstract

Abstract: The detection and recognition of traffic signs in automatic driving scene is very important. In order to improve the accuracy of traffic sign detection in the natural scene, this paper proposes an improved traffic sign recognition algorithm based on Cascade-RCNN. Firstly, the deep feature information of FPN module is fused into the shallow feature layer for the special task of small targets such as traffic signs. Secondly, the evaluation index IoU of the target detection task is improved by introducing in the direct evaluation index GIoU of the target detection task to guide the positioning task, which improves the detection accuracy. Finally, the algorithm is verified by experiments in GTSDB, a German traffic sign data set. When the network extraction is based on ResNet101 features, the mAP can reach 98.8%. The experimental results show that the proposed algorithm is effective and has superior engineering practical value

Key words: traffic sign detection, deep learning, convolutional neural network, Cascade-RCNN

Wang Hai, Wang Kuan, Cai Yingfeng, Liu Ze, Chen Long. Traffic Sign Recognition Based on Improved Cascade Convolution Neural Network[J].Automotive Engineering, 2020, 42(9): 1256-1262.

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Traffic Sign Recognition Based on Improved Cascade Convolution Neural Network

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