%0 Journal Article
%T The Lock Detection of Freight Train Based on RBF Neural Network and Genetic Algorithm<br>基于RBF神经网络和改进遗传算法的货车车锁检测
%A YAN Bo jun
%A CAI Ning tao
%A ZHENG Lian
%A WANG Ke yong
%A <br>严柏军
%A 蔡宁涛
%J 中国图象图形学报
%D 2002
%I 
%X To realize automatically detecting and recording system of freight trains, it's necessary to detect whether the locks of freight trains exist by the image of freight trains when they are entering railway stations. An approach of fast lock detection is put forward, which is based on RBF neural network and improved genetic algorithm. In this method, firstly extract the projection features of images, linear moment features of edge images, and the features of gray histograms of images, and these features reflect different characters of targets from different points of view, and then they are normalized to the input vector of RBF neural network, and RBF neural network is used for detection and location. At the same time, improved genetic algorithm is used to search the whole image, and searching process is speeded up because the genetic algorithm is a parallel and robust algorithm. In addition, image preprocessing is not done alone, and gray variation of images is eliminated during the process of feature extraction. Experiment results show that the method can overcome the problems of many types, deformation, and the variation of environmental brightness, and have the fast speed and high success rate of detection, and can be put into practical application. Therefore, the method has significant engineering value.
%K Feature extraction
%K RBF neural network
%K Genetic algorithm<br>货车
%K 车锁
%K 特征提取
%K RBF神经网络
%K 遗传算法
%K 铁路运输
%K 图象检测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=8034BBA92B3D23B9&yid=C3ACC247184A22C1&vid=DF92D298D3FF1E6E&iid=59906B3B2830C2C5&sid=F225282E8F5F1CBB&eid=AF8A4A632EC02431&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=11