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• W4291201881 abstract "Abstract Train rolling stock examination (TRSE) is a physical procedure for inspecting the bogie parts during transit at a little over 30 kmph. Currently, this process is manually performed across many railway networks across the world. This work proposes to automate the process of TRSE using artificial intelligence techniques. The previous works have proposed active contour-based models for the segmentation of bogie parts. Though accurate, the models require manual intervention and are found to be iterative making them unsuitable for real-time operations. In this work, we propose a segmentation model followed by a deep learning classifier that can accurately increase the deployability of such systems in real time. We apply the UNet model for the segmentation of bogie parts which are further classified using an attention-based convolutional neural network (CNN) classifier. In this work, we propose a shape deformable attention model to identify shape variations occurring in the video sequence due to viewpoint changes during the train movement. The TRSNet is trained and tested on the high-speed train bogie videos captured across four different trains. The results of the experimentation have been shown to improve the recognition accuracy of the proposed system by 6% over the state-of-the-art classifiers previously developed for TRSE." @default.
• W4291201881 created "2022-08-13" @default.
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• W4291201881 date "2022-08-13" @default.
• W4291201881 modified "2023-09-26" @default.
• W4291201881 title "Train rolling stock video segmentation and classification for bogie part inspection automation: a deep learning approach" @default.
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• W4291201881 doi "https://doi.org/10.1186/s44147-022-00128-x" @default.
• W4291201881 hasPublicationYear "2022" @default.
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