Optoelectronics Letters, Volume. 20, Issue 11, 681(2024)

Enhancing hyperspectral power transmission line defect and hazard identification with an improved YOLO-based model

Meng WANG... Long SUN, Jiong JIANG, Jinsong YANG and Xingru ZHANG |Show fewer author(s)
References(19)

[1] [1] NIE X T, TIAN J, WANG B, et al. Key technologies research on trimble ux5 vav photogrammetry[J]. Journal of North China University of Water Resources and Electric Power (natural science edition), 2020, 41(5): 73–83. (in Chinese)

[2] [2] ZHOU X Y, WANG K, LI L Y. A survey of deep learning-based object detection algorithms[J]. Electronic measurement technology, 2017, 40(11): 89–93. (in Chinese)

[3] [3] QIAN J J, WANG K, WANG R, et al. Task planning for intelligent substation robots[J]. Guangdong electric power, 2017, 30(2): 143–149. (in Chinese)

[4] [4] YU Y X. The urgency and long-term implementation of smart grids power[J]. System protection and control, 2019, 47(17): 1–5. (in Chinese)

[5] [5] NGUYEN V, JENSSEN R, ROVERSO D. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning[J]. International journal of electrical power and energy systems, 2018, 99: 107–120.

[6] [6] CHENG H, ZHAI Y, CHEN R, et al. Self-shattering defect detection of glass insulators based on spatial features[J]. Energies, 2019, 12(3): 543.

[7] [7] PAN X, ZHAO J, XU J. An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classification[J]. International journal of remote sensing, 2019: 1–25.

[8] [8] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, 2014, NW Washington, DC, USA. New York: IEEE, 2014: 508–587.

[9] [9] YIN Y, CHENG X, SHI F, et al. An enhanced lightweight convolutional neural network for ship detection in maritime surveillance system[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2022, 15: 5811–5825.

[10] [10] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18–22, 2023, Vancouver, Canada. New York: IEEE, 2023: 7464–7475.

[11] [11] ZHAO H, ZHANG H, ZHAO Y. YOLOv7-sea: object detection of maritime UAV images based on improved YOLOv7[C]//Proceedings of the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, January 2–7, 2023, Waikoloa, USA. New York: IEEE, 2023: 233–238.

[12] [12] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL]. (2022-07-06) [2023-08-22]. https://arxiv.org/abs/2207.02696.

[13] [13] BOCHKOVSKIY A, WANG C Y, LIAO H Y. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23) [2023-08-22]. https://arxiv.org/abs/2004.10934.

[14] [14] LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, October 10–17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 10012–10022.

[15] [15] MISRA D, NALAMADA T, HOU Q, et al. Rotate to attend: convolutional triplet attention module[EB/OL]. (2020-10-06) [2023-08-22]. https://arxiv.org/abs/2010.03045vl.

[16] [16] GEVORGYAN Z. Siou loss: more powerful learning for bounding box regression[EB/OL]. (2022-05-25) [2023-08-22]. https://arxiv.org/abs/2205.12740.

[17] [17] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2020: 2011–2023.

[18] [18] WANG Q, WU B, ZHU P, et al. ECA-net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, October 10–17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 11531–11539.

[19] [19] HENDERSON P, FERRARI V. End-to-end training of object class detectors for mean average precision[C]//13th Asian Conference on Computer Vision, November 20–24, 2016. Berlin, Heidelberg: Springer, 2017: 198–213.

Tools

Get Citation

Copy Citation Text

WANG Meng, SUN Long, JIANG Jiong, YANG Jinsong, ZHANG Xingru. Enhancing hyperspectral power transmission line defect and hazard identification with an improved YOLO-based model[J]. Optoelectronics Letters, 2024, 20(11): 681

Download Citation

EndNote(RIS)BibTexPlain Text
Save article for my favorites
Paper Information

Category: PAPERS

Received: Oct. 7, 2023

Accepted: Dec. 25, 2024

Published Online: Dec. 25, 2024

The Author Email:

DOI:10.1007/s11801-024-3213-3

Topics