Dust collector localization in trouble of moving freight car detection system  

作　　者：Fu-qiang ZHOU Rong ZOU He GAO 

机构地区：[1]School of Instrumentation Science and Optoelectronics Engineering,Beihang University

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2013年第2期98-106,共9页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (No. 61072134);the Research Fund for the Doctoral Program of Higher Education of China (No. 20101102110033)

摘　　要：For a long time,trouble detection and maintenance of freight cars have been completed manually by inspectors.To realize the transition from manual to computer-based detection and maintenance,we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system.Using mid-level features which are also named flexible edge arrangement(FEA) features,we first build the edge-based 2D model of the dust collectors,and then match target objects by a weighted Hausdorff distance method.The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively,which can truly reflect the most basic property regions of the 3D object.Experimental results indicate that the proposed algorithm has better robustness to variable lighting,different viewing angle,and complex texture,and it shows a stronger adaptive performance.The localization correct rate of the target object is over 90%,which completely meets the need of practical applications.For a long time,trouble detection and maintenance of freight cars have been completed manually by inspectors.To realize the transition from manual to computer-based detection and maintenance,we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system.Using mid-level features which are also named flexible edge arrangement（FEA） features,we first build the edge-based 2D model of the dust collectors,and then match target objects by a weighted Hausdorff distance method.The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively,which can truly reflect the most basic property regions of the 3D object.Experimental results indicate that the proposed algorithm has better robustness to variable lighting,different viewing angle,and complex texture,and it shows a stronger adaptive performance.The localization correct rate of the target object is over 90%,which completely meets the need of practical applications.

关 键 词：Hausdorff distance Weighting function Trouble detection Rail transportation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP274[自动化与计算机技术—计算机科学与技术]