Lightweight Vehicle Ranging Model Based on Faster RCNN
The vehicle accidents on the road refers to the different degrees of collision between vehicles,mostly because the vehicle does not maintain the safe driving distance,so in the actual road driving,the perception of vehicle distance is very important.In this paper,the target vehicle is identified based on Faster RCNN deep neural network.The Inception v2 model is used to adjust the original network structure,which can reduce the computational load while maintaining the target feature quantity and improve the model convergence speed.At the same time,based on the principle of data regression,the mapping model between image pixels and the actual distance is built,which implicitly solves the distortion problem in the imaging process of monocular camera.The experimental results show that the accuracy of vehicle identification by the established model reaches 82.83%,and the vehicle ranging error is less than 4%within the range of 40 meters in front,which can realize the distance judgment of the target vehicle in front,and provide theoretical basis for safe driving decision-making.
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