| DOI | Trouver le DOI : https://doi.org/10.1109/VTC2021-Spring51267.2021.9448887 |
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| Auteur | Rechercher : Mammeri, Abdelhamid1; Rechercher : Siddiqui, Abdul Jabbar1; Rechercher : Zhao, Yiheng1 |
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| Affiliation | - Conseil national de recherches du Canada. Automobile et les transports de surface
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| Format | Texte, Article |
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| Conférence | 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), April 25-28, 2021, Helsinki, Finland [Virtual Conference] |
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| Sujet | railway track segmentation; UAV imagery; UAV-based inspections |
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| Résumé | In the railway sector, track inspections are regularly needed to monitor the track conditions for potential hazards in order to ensure safety and security of life and property. Recently, conducting infrastructure inspections and monitoring using UAVs has gained attention in various industries including the railways. The rapid development of advanced deep learning and machine vision techniques have given rise to automated railway hazard detection systems based on UAV-based imagery. A major task in such systems is to localize or segment the railway tracks in UAV-based images. This paper investigates the effectiveness of a fully convolutional encoder-decoder type segmentation network called U-Net for the task of segmenting rail track regions from UAV-based images. Through experimental evaluations using a proprietary real-world dataset, we demonstrate U-Net's effectiveness in terms of mean Intersection over Union (IoU). Such methods of rail track segmentation are particularly useful in applications such as automated UAV navigation along rail tracks. |
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| Date de publication | 2021-06-15 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 60f96967-dbb3-42ca-9c74-6a0818071318 |
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| Enregistrement créé | 2021-12-09 |
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| Enregistrement modifié | 2021-12-09 |
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