1,193
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Improving power transmission tower state recognition in remote sensing images using cooperative Adaboost-MobileNet

, , , , , & show all
Pages 124-134 | Received 16 Aug 2022, Accepted 29 Nov 2022, Published online: 09 Jan 2023

References

  • Ahsan Md Manjurul, Tasfiq E. Alam, Theodore Trafalis, Pedro Huebner . 2020. “Deep MLP-CNN Model Using Mixed-Data to Distinguish Between COVID-19 and Non-COVID-19 Patients.” Symmetry 12 (9): 1526. doi:10.3390/sym12091526.
  • Awad, M. M., and M. Lauteri. 2021. “Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests.” Sustainability 13 (10): 1–5548. doi:10.3390/su13105548.
  • Carlos, T. B., and J. Kaminski Jr. 2017. “Dynamic Response Due to Cable Rupture in a Transmission Lines Guyed Towers.” Procedia Engineering 199: 116–121. doi:10.1016/j.proeng.2017.09.173.
  • Dileep, G. 2020. “A Survey on Smart Grid Technologies and Applications.” Renewable Energy 146: 2589–2625. doi:10.1016/j.renene.2019.08.092.
  • Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, et al. 2021. “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.” In International Conference on Learning Representations, Vienna, Austria, 3-7, May.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 26 June - 1 July, 770–778.
  • Howard, A., M. Sandler, G. Chu, L.C. Chen, B. Chen, M. Tan, W. Wang, et al. 2019. “Searching for MobileNetv3.” In IEEE/CVF Conference on Computer Vision, Seoul, Korea, 27 October - 2 November, 1–8.
  • Hu, J., L. Shen, and G. Sun. 2018. “Squeeze-And-Excitation Networks.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 18-22, June, 7132–7141.
  • Li, J., Y. Li, H. Jiang, and Q. Zhao. 2022. “Hierarchical Transmission Tower Detection from High-Resolution SAR Image.” Remote Sensing 14 (3): 1–625. doi:10.3390/rs14030625.
  • Liu, T., Y. Xiangbin, and B. Sun. 2018. “Combining Convolutional Neural Network and Support Vector Machine for Gait-Based Gender Recognition.” In Chinese Automation Congress, Xi’an, Beijing, 23-25, November, 3477–3481.
  • Lu, Z., H. Gong, Q. Jin, H. Qingwu, and S. Wang. 2022. “A Transmission Tower Tilt State Assessment Approach Based on Dense Point Cloud from UAV-Based LiDar.” Remote Sensing 14 (2): 1–408. doi:10.3390/rs14020408.
  • Matikainen, L., M. Lehtomaki, E. Ahokas, J. Hyyppa, M. Karjalainen, A. Jaakkola, A. Kukko, and T. Heinonen. 2016. “Remote Sensing Methods for Power Line Corridor Surveys.” Isprs Journal of Photogrammetry and Remote Sensing 119: 10–31. doi:10.1016/j.isprsjprs.2016.04.011.
  • Ma, N., X. Zhang, H.T. Zheng, and J. Sun. 2018. “Shufflenet V2: Practical Guidelines for Efficient CNN Architecture Design.” In European Conference on Computer Vision (ECCV), Munich, Germany, 8 - 14 September, 116–131.
  • Radosavovic, I., R. Prateek Kosaraju, R. B. Girshick, H. Kaiming, and P. Dollár. 2020. “Designing Network Design Spaces.” IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, 14-19, June 10425–10433.
  • Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv:1409.1556. doi:10.48550/arXiv.1409.1556. Accessed 3 January 2023.
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. “Going Deeper with Convolutions.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Boston, USA, 8-10 June, 1–8.
  • Taherkhani, A., G. Cosma, and T. M. McGinnity. 2020. “AdaBoost-CNN: An Adaptive Boosting Algorithm for Convolutional Neural Networks to Classify Multi-Class Imbalanced Datasets Using Transfer Learning.” Neurocomputing 404: 351–366. doi:10.1016/j.neucom.2020.03.064.
  • Xie, S., R. Girshick, P. Dollár, T. Zhuowen, and H. Kaiming 2017. “Aggregated Residual Transformations for Deep Neural Networks.” In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, 21-26 July, 1492–1500.