新疆大学信息科学与工程学院
纸质出版:2020
移动端阅览
[1]杨文忠,杨蒙蒙,温杰彬,等.基于One Class-SVM+Autoencoder模型的车辆碰撞检测[J].新疆大学学报(自然科学版)(中英文),2020,37(03):271-276+281.
[1]杨文忠,杨蒙蒙,温杰彬,等.基于One Class-SVM+Autoencoder模型的车辆碰撞检测[J].新疆大学学报(自然科学版)(中英文),2020,37(03):271-276+281. DOI: 10.13568/j.cnki.651094.651316.2020.02.26.0001.
DOI:10.13568/j.cnki.651094.651316.2020.02.26.0001.
为尽量避免车辆碰撞事故的发生
探索了机器学习和深度学习结合的方法
利用影响车辆碰撞的多个特征变量对车辆碰撞进行检测.首先使用皮尔逊相关性分析方法分析各个特征之间的关联度
接着使用One Class-SVM模型对数据集做"异常点"抛除操作.利用SMOTE(Synthetic Minority Over-sampling Technique)算法增加了少数类别的样本数量
最后采用自动编码器模型将影响车辆碰撞的因素(例如天气情况、光照情况等)作为模型的输入
通过解码器重构原始输入
获得输入与输出的最小重构误差计算阈值判断车辆碰撞情况.实验表明
数据经过One Class-SVM模型处理
再使用Autoencoder模型检测获得了比较好的测试结果.
The vehicle collision detection is very important to ensure the vehicle quality which is ultimately critical in protecting the passengers of the vehicle.This paper explores the method of combining machine learning and deep learning to detect vehicle collisions by using multiple feature variables that affect vehicle collisions.Firstly
Pearson correlation analysis method is used to analyze the correlation degree between each feature.Then
One Class-SVM model is used to remove "outliers" from the data set.The Synthetic Minority over-sampling Technique is used to increase the number of samples of minority categories
Finally
the auto encoder model takes the factors(such as weather conditions
light conditions
etc.) that affect the vehicle collision as the input of the model
reconstructs the original input through the decoder
and obtains the minimum reconstruction error of input and output to calculate the threshold to judge the vehicle collision.Experiments show that the data processed by One Class-SVM model
and then the Autoencoder model can achieve better detection results.
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