Railway wagon number recognition method based on combined GWO-SVM

Hongzhi Hu; Chao Chen; Fang Guan; Zhaohong Ren; Cuifeng Xu

doi:10.1117/12.2681330

8 June 2023 Railway wagon number recognition method based on combined GWO-SVM

Hongzhi Hu, Chao Chen, Fang Guan, Zhaohong Ren, Cuifeng Xu

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073Y (2023) https://doi.org/10.1117/12.2681330
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Aiming at the fundamental role of railway wagon number recognition in railway freight management and railway wagon inspection, a railway wagon number recognition method based on Combined Grey Wolf Optimizer Support Vector Machine (C-GWO-SVM) is proposed. Aiming at the confusing numbers and letters in the railway wagon number dataset, the algorithm in this article first uses a set of GWO-SVM to perform multi-classification processing on the railway wagon number characters, and divides the numbers and letters in the dataset into easily distinguishable number and letter characters, and easily confusing number and letter characters, with a classification accuracy of 98.875%; Then two sets of GWO-SVM are used to classify and recognize the numbers and letters in the railway wagon number characters, with classification accuracy of 99.70% and 99.99%, respectively. The experimental results show that compared with Sparrow Search Algorithm (SSA) to optimize SVM multi-classification algorithm, the GWO-SVM algorithm has shorter parameter optimization time, higher recognition accuracy and faster recognition speed in the application of railway wagon number and letter characters recognition.

Citation Download Citation

Hongzhi Hu, Chao Chen, Fang Guan, Zhaohong Ren, and Cuifeng Xu "Railway wagon number recognition method based on combined GWO-SVM", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073Y (8 June 2023); https://doi.org/10.1117/12.2681330

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KEYWORDS

Detection and tracking algorithms

Optical character recognition

Histograms of oriented gradient

Mathematical optimization

Data modeling

Statistical modeling

Education and training

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