Clustering and detection of good and bad rail line anchors from images

This 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: http://dx.doi.org/10.1109/ICCITechn.2015.7488072Absence 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%.Publishe