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dc.contributor.authorIslam, Samiul
dc.contributor.authorKhan, Rubayat Ahmed
dc.identifier.citationIslam, S., & Khan, R. A. (2015). Clustering and detection of good and bad rail line anchors from images. Paper presented at the 2015 18th International Conference on Computer and Information Technology, ICCIT 2015, 222-226. doi:10.1109/ICCITechn.2015.7488072en_US
dc.descriptionThis conference paper was presented in the International Conference on Computer and Information Technology, ICCIT 2015; Military Institute of Science and Technology (MIST)Mirpur CantonmentDhaka; Bangladesh; 21 December 2015 through 23 December 2015 [© 2015 IEEE] The conference paper's definite version is available at:
dc.description.abstractAbsence of railway anchors/fasteners is a serious concern as it might lead to severe consequences such as train derailments. Hence regular inspection is an obligation to ensure safety. The third world countries choose the inspection process to be non-automatic where a trained operator moves along the rail line boarding a motor trolley checking for visual anomalies. In the previous research [1], an automatic system was proposed to overcome the cons of the running manual technique by using image processing. Two feature detection algorithms - Shi Tomasi and Harris Stephen - were used and an accuracy of 83.55% was achieved. This research presents an upgraded version of the previous work by introducing Neural Network. The addition of NN has not only speeded up the detection process but increased the accuracy significantly to approximately 93.86%.en_US
dc.publisher© 2015 Institute of Electrical and Electronics Engineers Inc.en_US
dc.subjectFeature detectionen_US
dc.subjectFeature extractionen_US
dc.subjectNeural networksen_US
dc.titleClustering and detection of good and bad rail line anchors from imagesen_US
dc.typeConference paperen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University

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