Vehicle Intelligent Classification Based on Big Multimodal Data Analysis and Sparrow Search Optimization

Abstract

Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.