| 摘 要: 针对目前智能交通系统实时道路车辆检测中存在的检测效率不高等问题，设计了一种基于深度学习的道路车辆检测算法。首先在搭建好的平台下，通过卷积神经网络对采集的车辆图像数据集进行训练，得到训练后的模型；其次，对该模型内部的层结构进行可视化；最后，通过调节各网络参数及层结构对该模型进行优化。训练的模型通过实验测试，分别对图片和视频进行检测，图像识别准确率高，检测速度快，跟踪精度高，可应用于实时交通系统的检测。 |
| 关键词: 深度学习；卷积神经网络；车辆识别；OpenCV |
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中图分类号: TP183
文献标识码: A
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| Design of Vehicle Detection System based on Deep Learning |
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LI Xiong1, CHEN Yucong2
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( 1.Xi 'an Institute of Electromechanical Information Technology, Xi 'an 710065, China;
2.School of Computing, Xianyang Normal University, Xianyang 712000, China )
42340658@qq.com; 943858467@qq.com
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| Abstract: Aiming at the low detection efficiency of real-time road vehicle detection in intelligent transportations system, this paper proposes to design a road vehicle detection algorithm based on deep learning. Firstly, based on the built platform, convolutional neural network is used to train the collected vehicle image dataset to obtain the trained model. Secondly, the layer structure inside the model is visualized. Finally, the model is optimized by adjusting all of the network parameters and layer structure. The trained model has been tested by experiments to detect pictures and videos respectively. Experiment results show that the proposed model can be applied to the detection of real-time traffic systems with high image recognition accuracy, fast detection speed, and high tracking accuracy. |
| Keywords: deep learning; convolutional neural network; vehicle recognition; OpenCV |
