Abstract
From past to present, railway transportation is frequently preferred by people due to its economic and safe nature. TCDD personnel carry out the soundness control of the railway tracks by visual inspection. Visual inspection takes a long time. Performing the inspection process of the rails autonomously by the computer will speed up the detection of defective rails and minimize the risk of railroad accidents. In this study, automatic detection of defects in the rails is provided with the CNN architecture. In the study, a total of 2000 images belonging to two classes, broken and intact, were used. With the GoogleNet CNN architecture, the ray images were classified correctly at a rate of 96.5%. The study can help businesses in the inspection of the rails for the safety of railway transportation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
