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.
Highlights
IntroductionThe classification of vehicles is mainly within the line-of-sight range. Vehicles are distinguished based on the degree of road damage and occupancy in the process of driving, which is the basis for traffic statistics, road toll determination, and vehicle restrictions
At present, the classification of vehicles is mainly within the line-of-sight range
Using evolutionary rules to guide the integration of neural networks, we propose an artificial neural network group
Summary
The classification of vehicles is mainly within the line-of-sight range. Vehicles are distinguished based on the degree of road damage and occupancy in the process of driving, which is the basis for traffic statistics, road toll determination, and vehicle restrictions. This article takes the acoustic signals in the vehicle driving process as research objects and uses the Artificial Neural Network (BNNG) algorithm to realize automatic classification of vehicles under non-line-of-sight environments[1]. The research on neural network integration mainly focuses on two aspects: how to generate an integrated network and how to determine the output rules. This paper finds that there are certain similarities between the neural network integration and the evolution model of the ecosystem. For this purpose, the ecosystem model is predicted and matched with the parameters in the neural network integration. Using evolutionary rules to guide the integration of neural networks, we propose an artificial neural network group
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