基于AlexNet算法的道路能见度估测方法

Road Visibility Estimation Method Based on AlexNet Algorithm

本文采用AlexNet神经网络算法构建一个高速公路能见度识别的框架,通过对道路摄像头图像的采集,对图像进行标注、对AlexNet算法进行训练,提取图像能见度特征,构建能见度等级识别模型,实时接入道路摄像头图像,实现能见度值的估测。通过对安徽省高速公路42个监控摄像机进行图像的采集,抽取标注有能见度值的15万余幅样本,进行能见度识别结果分析,结果显示平均识别率达到78.02%,其中有14个站点的识别率超过90%,21个站点的识别率在80%以上。基于AlexNet算法的道路能见度估测方法能够满足道路能见度实时性和识别准确率的要求,可以作为能见度仪未安装地区的能见度辅助监测方法,同时对于光照变化、远近距离等都具有良好的鲁棒性。

In this paper,AlexNet neural network algorithm is used to construct a framework of highway visibility recognition.Through the collection of road camera images,the images are labeled,the AlexNet algorithm is trained,image visibility characteristics are extracted,the visibility recognition model is constructed,and the road camera image is accessed in real time to realize the estimation of visibility values.The visibility recognition results are analyzed on 150000 samples labeled with visibility value extracted from 42 surveillance cameras in Anhui province.The results show that the average recognition rate of 42 points is 78.02%.Among them,14 sites have more than 90% recognition rate and 21 sites have more than 80% recognition rate.The road visibility estimation method based on AlexNet algorithm satisfies the requirements of road visibility real-time and recognition accuracy,and can be used as an auxiliary visibility monitoring method in areas where the visibility meter is not installed.Meanwhile,it has good robustness to illumination changes,distance,and so on.