基于改进蚁群算法的轨道缺陷图像分类  

Track Defect Image Classification Based on Improved Ant Colony Algorithm

作　　者：曹义亲[1] 武丹 黄晓生[1] CAO Yi-qin;WU Dan;HUANG Xiao-sheng(School of Software,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学软件学院

出　　处：《计算机科学》2019年第8期292-297,共6页Computer Science

基　　金：江西省科技支撑计划重点项目(20161BBE50081)资助

摘　　要：针对传统方法分类准确性低、分类速度慢且不同轨道缺陷类型的识别准确性有很大差异的弊端,提出一种新的基于改进蚁群算法的轨道缺陷图像分类方法。对轨道缺陷图像进行预处理,利用竖直投影法对轨道表面区域进行提取,将模糊理论和超熵理论结合在一起获取最佳分割阈值,完成图像分割。结合自适应阈值Canny边缘检测算子和Hough转换法,确定轨道缺陷部分。对缺陷部分的边缘细节进行改进,使轨道缺陷部分的轮廓更加显著。对轨道缺陷特征进行提取,在此基础上分析了基本蚁群算法,针对基本蚁群算法容易陷入局部最优的弊端进行改进,将与特征相似性最高作为判别函数,采用改进蚁群算法对轨道缺陷图像进行分类。实验结果表明,所提方法的分类准确度高,且分类速度快。In view of the disadvantages of the traditional methods,such as low accuracy,slow classification speed,and a great difference in the recognition accuracy of different types of track defects,a new method of track defect image classification based on improved ant colony algorithm was proposed.The track defect image is preprocessed,the vertical projection method is used to extract the track surface area,the fuzzy theory and the hyper entropy theory are combined to obtain the best segmentation threshold,and the image segmentation is completed.Combined with adaptive threshold Canny edge detection operator and Hough transformation method,the rail defect part is determined.The edge details of defects are improved to make the contour of track defects more obvious.On the basis of this,the basic ant colony algorithm is analyzed,the characteristic similarity is used as a discriminant function,and the improved ant colony algorithm is used to classify the track defect image.Experimental results show that the classification accuracy and classification speed of the proposed method are high.

关 键 词：改进蚁群算法 轨道 缺陷图像 分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]