Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning
Power line detection is necessary for the safe flight of low-flying UAVs (Unmanned Aerial Vehicles). This paper deals with the power line recognition problem for the safety of agricultural spraying drones in agricultural environments. The dataset of power lines was obtained in an agricultural enviro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9780149/ |
| _version_ | 1818250500603117568 |
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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 detection is necessary for the safe flight of low-flying UAVs (Unmanned Aerial Vehicles). This paper deals with the power line recognition problem for the safety of agricultural spraying drones in agricultural environments. The dataset of power lines was obtained in an agricultural environment. The training dataset was constructed by labeling powerlines with bounding boxes of 6 sizes, ranging from 0.03 to 0.15 times the image. The model used for training was the tiny-YOLOv3 model. The model was verified using the mean average precision (mAP), which was used to verify the object recognition performance. Depending on the six sizes of bounding boxes, the mAPs were evaluated to be 70.22, 94.00, 86.75, 68.87, 61.65, and 53.40, respectively. The mAP was the highest at the bounding box of 0.05 times the image size, and it was confirmed that this size is most suitable for power line detection. The real-time frames per second (FPS) results of power lines detection are on average 12.5. This paper shows that the location detection of power lines is possible in real-time using deep-learning techniques with embedded systems. |
| first_indexed | 2024-12-12T15:53:23Z |
| format | Article |
| id | doaj.art-682f3f50204f484bbf9485ae722f7494 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T15:53:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-682f3f50204f484bbf9485ae722f74942022-12-22T00:19:32ZengIEEEIEEE Access2169-35362022-01-0110549475495610.1109/ACCESS.2022.31771969780149Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep LearningHyun-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 KoreaSmart Agricultural Machinery R&D Group, Korea Institute of Industrial Technology, Gimje-si, Jeollabuk-do, Republic of KoreaSmart Agricultural Machinery R&D 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 detection is necessary for the safe flight of low-flying UAVs (Unmanned Aerial Vehicles). This paper deals with the power line recognition problem for the safety of agricultural spraying drones in agricultural environments. The dataset of power lines was obtained in an agricultural environment. The training dataset was constructed by labeling powerlines with bounding boxes of 6 sizes, ranging from 0.03 to 0.15 times the image. The model used for training was the tiny-YOLOv3 model. The model was verified using the mean average precision (mAP), which was used to verify the object recognition performance. Depending on the six sizes of bounding boxes, the mAPs were evaluated to be 70.22, 94.00, 86.75, 68.87, 61.65, and 53.40, respectively. The mAP was the highest at the bounding box of 0.05 times the image size, and it was confirmed that this size is most suitable for power line detection. The real-time frames per second (FPS) results of power lines detection are on average 12.5. This paper shows that the location detection of power lines is possible in real-time using deep-learning techniques with embedded systems.https://ieeexplore.ieee.org/document/9780149/Power line detectiondeep learningagricultural spraying droneunmanned aerial vehicle (UAV) |
| spellingShingle | Hyun-Sik Son Deok-Keun Kim Seung-Hwan Yang Young-Kiu Choi Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning IEEE Access Power line detection deep learning agricultural spraying drone unmanned aerial vehicle (UAV) |
| title | Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning |
| title_full | Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning |
| title_fullStr | Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning |
| title_full_unstemmed | Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning |
| title_short | Real-Time Power Line Detection for Safe Flight of Agricultural Spraying Drones Using Embedded Systems and Deep Learning |
| title_sort | real time power line detection for safe flight of agricultural spraying drones using embedded systems and deep learning |
| topic | Power line detection deep learning agricultural spraying drone unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/9780149/ |
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