A vehicle classification method based on improved ResNet

Kaiyan Zhang; Xinglong Feng

doi:10.1117/12.2673306

13 April 2023 A vehicle classification method based on improved ResNet

Kaiyan Zhang, Xinglong Feng

Proceedings Volume 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022); 1260506 (2023) https://doi.org/10.1117/12.2673306
Event: Second Conference on High Performance Computing and Communication Engineering, 2022, Harbin, China

Abstract

While autonomous driving has made great strides, there are still some scenarios that need to be solved. For different kinds of vehicles, it is necessary to classify them accurately so as to adopt different driving strategies. At present, due to the good performance of deep learning in the field of image recognition, this method is mainly used for image classification. Therefore, facing the problem of vehicle classification, this paper adopts the method of combining EcaNet and ResNet to classify ten common vehicles in automatic driving perception. The experimental results show that the classification accuracy of the proposed method is 75.83%, compared with 66.46% of the comparison method.

Citation Download Citation

Kaiyan Zhang and Xinglong Feng "A vehicle classification method based on improved ResNet", Proc. SPIE 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022), 1260506 (13 April 2023); https://doi.org/10.1117/12.2673306

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