基于神经网络的红绿灯识别研究

Traffic light judgment design based on Neural Network

交通灯的识别对人工智能以及无人驾驶都有着举足轻重的作用,本文研究交通识别中的红绿灯判断,用于改善驾驶员疲劳以及维护交通秩序从而提高驾驶安全系数减少交通事故的发生。通过机器视觉采集红绿灯交通信号图,运用M atlab进行图片处理截取红绿灯区域,提取每张图片的121个像素点RGB值,运用1和2分别表示绿灯和红灯,建立红绿灯样本训练库,通过Matlab自带的数据拟合工具nftool进行神经网络训练,调整训练、验证、测试数据比例,最终得到识别效果较好的神经网络算法。运用样本均值进行测试也能够得到较好的识别。

The identification of traffic lights plays a decisive role in both artificial intelligence and unmanned driving.This paper studies the judgment of traffic lights in traffic identification,which can be used to improve driver fatigue and maintain traffic order so as to improve the driving safety coefficient and reduce the occurrence of traffic accidents.By machine vision gathering traffic signal of traffic light,using Matlab image processing to crop traffic area,in each image RGB values of 121 pixels are extracted,1 and 2 are used to represent the traffic lights respectively,and red and green traffic light sample training library in built,through the Matlab’s own data fitting tool nftool neural network training is conducted,the ratio among training,validation,test data is adjusted,so the neural network algorithm with better identification effect is achieved.Using the sample mean for testing can also be well identified.