Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing
Power line collisions pose a significant threat to the safety of drones. This is because it is difficult for drone pilots to recognize power lines at long distances, even on sunny days, and power lines are less visible in rainy or foggy weather. Therefore, power line detection is necessary for safe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10145454/ |
| _version_ | 1797803124230979584 |
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| author | Hyun-Sik Son Deok-Keun Kim Seung-Hwan Yang Young-Kiu Choi |
| author_facet | Hyun-Sik Son Deok-Keun Kim Seung-Hwan Yang Young-Kiu Choi |
| author_sort | Hyun-Sik Son |
| collection | DOAJ |
| description | Power line collisions pose a significant threat to the safety of drones. This is because it is difficult for drone pilots to recognize power lines at long distances, even on sunny days, and power lines are less visible in rainy or foggy weather. Therefore, power line detection is necessary for safe drone flight. This article proposes an algorithm that can recognize various shapes and locations of multiple power lines while improving the recognition performance of power lines compared to previous studies. YOLO, a deep learning technology used for object detection, is used to recognize power lines as multiple bounding boxes, and center points of these bounding boxes are sorted and integrated. This algorithm improves the power line detection performance by excluding incorrectly detected power lines and restoring undetected parts of the power lines. The performance of the proposed method was evaluated using the intersection-over-union (IoU) and F1-score, which were 0.674 and 0.528, respectively. This performance was superior to those of U-Net, LaneNet and BiSeNet V2 which are deep learning technologies for segmentation. The proposed method was mounted on the embedded system of the test drone, and tests were conducted indoor and outdoor. Then, the average frames per second (FPS) value was calculated as 10.05. Various shapes and locations of multiple power lines can be recognized in real-time using the power line recognition method proposed in this paper. |
| first_indexed | 2024-03-13T05:16:07Z |
| format | Article |
| id | doaj.art-60e9a09f35ea443c897921fdf3fa11d3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T05:16:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-60e9a09f35ea443c897921fdf3fa11d32023-06-15T23:00:54ZengIEEEIEEE Access2169-35362023-01-0111578955790410.1109/ACCESS.2023.328361310145454Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-ProcessingHyun-Sik Son0https://orcid.org/0000-0003-0409-7391Deok-Keun Kim1https://orcid.org/0000-0002-8354-311XSeung-Hwan Yang2https://orcid.org/0000-0002-1067-7280Young-Kiu Choi3https://orcid.org/0000-0003-3726-6497Department of Electrical and Computer Engineering, Pusan National University, Geumjeong-gu, Busan, Republic of KoreaInterdisciplinary Program in Agricultural and Life Science, Chonnam National University, Buk-gu, Gwangju, Republic of KoreaSmart Agricultural Machinery Research and Development Group, Korea Institute of Industrial Technology, Gimje-si, Jeollabuk-do, Republic of KoreaDepartment of Electrical and Computer Engineering, Pusan National University, Geumjeong-gu, Busan, Republic of KoreaPower line collisions pose a significant threat to the safety of drones. This is because it is difficult for drone pilots to recognize power lines at long distances, even on sunny days, and power lines are less visible in rainy or foggy weather. Therefore, power line detection is necessary for safe drone flight. This article proposes an algorithm that can recognize various shapes and locations of multiple power lines while improving the recognition performance of power lines compared to previous studies. YOLO, a deep learning technology used for object detection, is used to recognize power lines as multiple bounding boxes, and center points of these bounding boxes are sorted and integrated. This algorithm improves the power line detection performance by excluding incorrectly detected power lines and restoring undetected parts of the power lines. The performance of the proposed method was evaluated using the intersection-over-union (IoU) and F1-score, which were 0.674 and 0.528, respectively. This performance was superior to those of U-Net, LaneNet and BiSeNet V2 which are deep learning technologies for segmentation. The proposed method was mounted on the embedded system of the test drone, and tests were conducted indoor and outdoor. Then, the average frames per second (FPS) value was calculated as 10.05. Various shapes and locations of multiple power lines can be recognized in real-time using the power line recognition method proposed in this paper.https://ieeexplore.ieee.org/document/10145454/Power line detectioncontinuous objectsegmentationagricultural spraying droneunmanned aerial vehicle (UAV) |
| spellingShingle | Hyun-Sik Son Deok-Keun Kim Seung-Hwan Yang Young-Kiu Choi Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing IEEE Access Power line detection continuous object segmentation agricultural spraying drone unmanned aerial vehicle (UAV) |
| title | Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing |
| title_full | Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing |
| title_fullStr | Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing |
| title_full_unstemmed | Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing |
| title_short | Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing |
| title_sort | recognition of the shape and location of multiple power lines based on deep learning with post processing |
| topic | Power line detection continuous object segmentation agricultural spraying drone unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/10145454/ |
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