Evolutionary Neural Architecture Search for Traffic Sign Recognition
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Abstract:
Convolutional neural networks (CNNs) exhibit superior performance in image feature extraction, making them exten-sively used in the area of traffic sign recognition. However, the design of existing traffic sign recognition algorithms of-ten relies on expert knowledge to enhance the image feature extraction networks, necessitating image preprocessing and model parameter tuning. This increases the complexity of the model design process. This study introduces an evolution-ary neural architecture search algorithm for the automatic design of neural network models tailored for traffic sign recog-nition. By integrating the construction parameters of ResNet into evolutionary algorithms (EAs), we automatically gener-ate lightweight networks for traffic sign recognition, utilizing blocks as the fundamental building units. Experimental evaluations on the German traffic sign dataset reveal that the algorithm attains a recognition accuracy of 99.32%, with a mere 2.8?106 parameters. Experimental results comparing the proposed method with other traffic sign recognition algo-rithms demonstrate that the method can more efficiently discover neural network architectures, significantly reducing the number of network parameters while maintaining recognition accuracy.
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Project Supported:
The National Natural Science Foundation of China(number: 62066041)
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