基于卷积神经网络车辆行驶的识别预警系统

Vehicle running recognition and early warning system based on convolutional neural networks

随着人们的生活水平不断提高,汽车的使用更具普遍性。为了使汽车的行驶安全得到保障,使车辆行驶的识别预警系统性能得到提高,对卷积神经网络的模型与作用机制进行了剖析,并进行了相关的车辆识别预警实验。结果显示,基于卷积神经网络车辆行驶的识别预警系统具有较强的优越性与可行性,其识别检测耗时仅需要1.5356 s,且准确率较高。将卷积神经网络模型应用至车辆行驶的识别预警中,能够有效防止交通安全事故的发生,促进智能交通系统的可持续发展。

With the continuous improvement of people's living standards,the use of cars is more universal.In order to ensure the safety of the vehicle and improve the performance of the vehicle recognition and early warning system,this study analyzes the model and mechanism of the convolutional neural network,and conducts experiments of relevant vehicle recognition and early warning.The results show that the recognition and early warning system based on the convolutional neural network has strong advantages and feasibility.The recognition and detection time is only 1.5356 seconds,and the accuracy is relatively high.the application of the convolutional neural network model to recognition and early warning of the running vehicle can effectively prevent traffic safety accidents and promote the sustainable development of intelligent transportation system.