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Vehicle Detection and Classification based on Deep Neural Network for Intelligent Transportation Applications

机译：基于深度神经网络的车辆智能检测与分类

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摘要

This paper proposes an optimized vehicle detection and classification method based on deep learning technology for intelligent transportation applications. We optimize the Convolutional Neural Network (CNN) architecture by fine-tuning the existing CNN architecture for the intelligent transportation applications. The proposed design achieves the accuracy of miss rate around 10% when FPPI is 0.1. Realized on nVidia Titan-X GPU, the proposed design can reach the performance about 720×480 video under different weather condition (day, night, raining) at 25fps. The proposed model can achieve 90% accuracy on three target vehicle classes including small vehicles (Sedan, SUV, Van), big vehicles (Bus) and Trucks.
机译：本文提出了一种基于深度学习技术的智能运输应用的优化车辆检测和分类方法。我们通过微调现有的CNN架构来优化卷积神经网络（CNN）架构进行智能运输应用。当FPPI为0.1时，所提出的设计达到了10 \％左右的错过率的准确性。在NVIDIA Titan-X GPU上实现，建议的设计可以在25fps的不同天气状况（日，夜晚，下雨）下大约720×480视频的性能。拟议的模型可以在三个目标车型上实现90 \％的精度，包括小型车辆（轿车，SUV，VAN），大型车辆（公共汽车）和卡车。

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