Sensors, Vol. 19, Pages 4021: Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Sensors, Vol. 19, Pages 4021: Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles Sensors doi: 10.3390/s19184021 Authors: Jingwei Cao Chuanxue Song Silun Peng Feng Xiao Shixin Song Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. C...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research