Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks
Object detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tra...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/7/370 |
| _version_ | 1827619327504809984 |
|---|---|
| author | Rohan Sharma Kishan Patel Sanyami Shah Michal Aibin |
| author_facet | Rohan Sharma Kishan Patel Sanyami Shah Michal Aibin |
| author_sort | Rohan Sharma |
| collection | DOAJ |
| description | Object detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tracks operated by Via Rail Canada. Via Rail operates about 500 trains running on 12,500 km of tracks. These tracks pass through long stretches of sparsely populated lands. Maintaining these tracks is challenging due to the sheer amount of resources required to identify the points of interest (POI), such as growing vegetation, missing or broken ties, and water pooling around the tracks. We aim to use the YOLO algorithm to identify these points of interest with the help of aerial footage. The solution shows promising results in detecting the POI based on unmanned aerial vehicle (UAV) images. Overall, we achieved a precision of 74% across all POI and a mean average precision @ 0.5 (mAP @ 0.5) of 70.7%. The most successful detection was the one related to missing ties, vegetation, and water pooling, with an average accuracy of 85% across all three POI. |
| first_indexed | 2024-03-09T10:24:43Z |
| format | Article |
| id | doaj.art-d632a85d520747428edfbdd40363b23e |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T10:24:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-d632a85d520747428edfbdd40363b23e2023-12-01T21:45:24ZengMDPI AGAerospace2226-43102022-07-019737010.3390/aerospace9070370Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway TracksRohan Sharma0Kishan Patel1Sanyami Shah2Michal Aibin3Khoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, CanadaKhoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, CanadaKhoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, CanadaKhoury College of Computer Sciences, Northeastern University, 410 West Georgia Street, Vancouver, BC V6B 1Z3, CanadaObject detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tracks operated by Via Rail Canada. Via Rail operates about 500 trains running on 12,500 km of tracks. These tracks pass through long stretches of sparsely populated lands. Maintaining these tracks is challenging due to the sheer amount of resources required to identify the points of interest (POI), such as growing vegetation, missing or broken ties, and water pooling around the tracks. We aim to use the YOLO algorithm to identify these points of interest with the help of aerial footage. The solution shows promising results in detecting the POI based on unmanned aerial vehicle (UAV) images. Overall, we achieved a precision of 74% across all POI and a mean average precision @ 0.5 (mAP @ 0.5) of 70.7%. The most successful detection was the one related to missing ties, vegetation, and water pooling, with an average accuracy of 85% across all three POI.https://www.mdpi.com/2226-4310/9/7/370UAVrailwaycomputer vision |
| spellingShingle | Rohan Sharma Kishan Patel Sanyami Shah Michal Aibin Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks Aerospace UAV railway computer vision |
| title | Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks |
| title_full | Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks |
| title_fullStr | Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks |
| title_full_unstemmed | Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks |
| title_short | Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks |
| title_sort | aerial footage analysis using computer vision for efficient detection of points of interest near railway tracks |
| topic | UAV railway computer vision |
| url | https://www.mdpi.com/2226-4310/9/7/370 |
| work_keys_str_mv | AT rohansharma aerialfootageanalysisusingcomputervisionforefficientdetectionofpointsofinterestnearrailwaytracks AT kishanpatel aerialfootageanalysisusingcomputervisionforefficientdetectionofpointsofinterestnearrailwaytracks AT sanyamishah aerialfootageanalysisusingcomputervisionforefficientdetectionofpointsofinterestnearrailwaytracks AT michalaibin aerialfootageanalysisusingcomputervisionforefficientdetectionofpointsofinterestnearrailwaytracks |