PLPose: An efficient framework for detecting power lines via key points-based pose estimation
The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and t...
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King Saud bin Abdulaziz University
2023
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oai:scholars.utp.edu.my:375022023-10-04T13:22:59Z http://scholars.utp.edu.my/id/eprint/37502/ PLPose: An efficient framework for detecting power lines via key points-based pose estimation Jaffari, R. Hashmani, M.A. Reyes-Aldasoro, C.C. Junejo, A.Z. Taib, H. Abdullah, M.N.B. The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (�29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/plmmpose). © 2023 The Author(s) King Saud bin Abdulaziz University 2023 Article NonPeerReviewed Jaffari, R. and Hashmani, M.A. and Reyes-Aldasoro, C.C. and Junejo, A.Z. and Taib, H. and Abdullah, M.N.B. (2023) PLPose: An efficient framework for detecting power lines via key points-based pose estimation. Journal of King Saud University - Computer and Information Sciences, 35 (7). ISSN 13191578 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165244866&doi=10.1016%2fj.jksuci.2023.101615&partnerID=40&md5=0c3ab6cf3accda6274e486d86d48b1e7 10.1016/j.jksuci.2023.101615 10.1016/j.jksuci.2023.101615 10.1016/j.jksuci.2023.101615 |
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The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (�29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/plmmpose). © 2023 The Author(s) |
| format |
Article |
| author |
Jaffari, R. Hashmani, M.A. Reyes-Aldasoro, C.C. Junejo, A.Z. Taib, H. Abdullah, M.N.B. |
| spellingShingle |
Jaffari, R. Hashmani, M.A. Reyes-Aldasoro, C.C. Junejo, A.Z. Taib, H. Abdullah, M.N.B. PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| author_facet |
Jaffari, R. Hashmani, M.A. Reyes-Aldasoro, C.C. Junejo, A.Z. Taib, H. Abdullah, M.N.B. |
| author_sort |
Jaffari, R. |
| title |
PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| title_short |
PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| title_full |
PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| title_fullStr |
PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| title_full_unstemmed |
PLPose: An efficient framework for detecting power lines via key points-based pose estimation |
| title_sort |
plpose: an efficient framework for detecting power lines via key points-based pose estimation |
| publisher |
King Saud bin Abdulaziz University |
| publishDate |
2023 |
| url |
http://scholars.utp.edu.my/id/eprint/37502/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165244866&doi=10.1016%2fj.jksuci.2023.101615&partnerID=40&md5=0c3ab6cf3accda6274e486d86d48b1e7 |
| _version_ |
1779441393392943104 |
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13.211869 |