BNNG Algorithm Modeling for Vehicle Classification Recognition under Non Line-of -sight Environment

¹ School of Mechanical and Electrical Engineering Huainan Normal University, 232038, Huainan, China
² Research Center for Power Supply and Control Engineering Technology of Rail Transit, 232038, Huainan, China.

^* Corresponding author: Wu long: fjq5060912@126.com

Abstract

At present, the automatic classification of vehicles on roads is mostly based on image recognition, and there are defects in adaptability under non-line-of-sight environments. In this paper, based on the similarity of the integration of the ecosystem model and multi-neural network model, an artificial neural network group (BNNG) algorithm was proposed. The vehicle’s driving acoustic signal was taken as the research object, and it was calculated using the Artificial Neural Network (BNNG) algorithm to achieve automatic classification and recognition of vehicle models. Through experimental tests, it is shown that under non-line-of-sight environments, the accuracy of vehicle classification can be improved, and the misrecognition rate of similar models can be greatly reduced. This provided a new method for the automatic classification and identification of vehicles on roads, which was of great significance to monitor vehicle safety in non-line-of-sight environments.