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| Title: | 基於深度學習框架之電塔與絕緣子檢測
Power Tower and Insulator Detection Based on Deep Learning Framework |
| Authors: | 陳冠增
Chen, Kuan-Tseng |
| Contributors: | 廖文宏
Liao, Wen-Hung
陳冠增
Chen, Kuan-Tseng |
| Keywords: | 電腦視覺
物件偵測
深度學習
Computer vision
Object detection
Deep learning |
| Date: | 2021 |
| Issue Date: | 2021-09-02 16:53:35 (UTC+8) |
| Abstract: | 深度學習技術改變了電腦視覺領域的理論與實作架構，近年來已經被廣泛地應用，本論文主要的目標是將這些新興的技術道入電塔與絕緣子的自動偵測任務，除了蒐集更多視角的電塔資料，也針對不同的網路架構、骨幹與優化方式，進行全面式的分析比較，提升對電塔與絕緣子的辨識準確率，以期運用於無人機的安全檢測。
本論文共實驗CascadeRCNN、FasterRCNN、Retinanet、Fcos、DetectoRS、YOLO V3與YOLO V4七種架構，搭配Resnet50、Resnet101、Regnet、Resnest、Resnext與Res2net六個骨架模型，測試七個優化方法，分別為Libra-RCNN、Soft-nms、Weight Standardization、OHEM、DCNv2、Multi-scale training與Multi-scale testing，以尋找最佳組合。
在電塔的檢測中，本論文發現最佳組合為CascadeRCNN架構，搭配Resnext、Weight Standardization與Multi-scale training，取得AP = 91.06%、AR = 93.72%以及F1 Score = 92.37%。而絕緣子的最佳組合為DetectoRS架構，搭配Regnet、Weight Standardization與Multi-scale training，取得AP = 95.66%、AR = 97.92%以及F1 Score = 96.78%，最終的準確率與現有方法相比，都獲得相當程度的提升。
Deep learning technology has brought forth a paradigm shift in the field of computer vision in both theoretical and practical aspects. It has been widely incorporated in many applications recently. The main objective of this research is to introduce these emerging technologies into the automatic detection of power towers and insulators. Toward this goal, we investigate the combination of different network architectures, backbones and optimization methods to compare and analyze their performance in the identification of power towers and insulators, with an expectation to apply them to the safety inspection using UAVs.
In this thesis, we tested seven architectures, including CascadeRCNN, FasterRCNN, Retinanet, Fcos, DetectoRS, YOLO V3 and YOLO V4, with six backbone models, i.e., Resnet50, Resnet101, Regnet, Resnest, Resnext and Res2net, and seven optimization methods, namely Libra- RCNN, Soft-nms, Weight Standardization, OHEM, DCNv2, Multi-scale training and Multi-scale testing to search for the best combination.
In the detection of power towers, we found that the best combination is CascadeRCNN with Resnext, Weight Standardization and Multi-scale training, achieving results of AP = 91.06%, AR = 93.72% and F1 Score = 92.37%. The best combination for insulator detection is DetectoRS, combined with Regnet, Weight Standardization and Multi-scale training, obtaining results of AP = 95.66%, AR = 97.92% and F1 Score = 96.78%. The final accuracy shows notable improvements over existing methods. |
| Reference: | 1.ImageNet Large Scale Visual Recognition Competition (ILSVRC). http://www.image-net.org/challenges/LSVRC/, last visited on Dec 2018.
2.ImageNet. http://www.image-net.org/, last visited on Dec 2018.
3.C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks, arXiv preprint arXiv:1312.6199, 2014.
4.Warren S. McCulloch, Walter H. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115-133, 1943.
5.Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386-408, 1958.
6.B. Mehlig . Artificial neural network. arXiv:1901.05639.
7.Rumelhart, D. E., Hinton, G. E., Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536, 1986.
8.Michael Nielsen. Neural Networks and Deep Learning. http://neuralnetworksanddeeplearning.com/index.html. Last visited on Dec 2018.
9.Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
10.Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." arXiv preprint arXiv:1709.01507 7 (2017).
11.Park, E., et al. "ILSVRC-2017." URL http://www. image-net. org/challenges/LSVRC/2017 (2017).
12.Waibel, Alexander, et al. "Phoneme recognition using time-delay neural networks." Readings in speech recognition. 1990. 393-404.
13.LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.
14.S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
15.O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Med.Image Comput. Comput. Assist. Intervent., Oct. 2015, pp. 234–241.
16.Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., ... & Zhang, Z. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155.
17.Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollar, and Kaiming He. Detectron. ´ https://github.com/facebookresearch/detectron, 2020.
18.Francisco Massa and Ross Girshick. maskRCNN-benchmark:Fast, modular reference implementation of instance segmentation and object detection algorithms in pytorch.https://github.com/facebookresearch/maskRCNN-benchmark, 2020.
19.Xian Tao , Dapeng Zhang, Zihao Wang , Xilong Liu , Hongyan Zhang, and De Xu. Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks, Senior Member, IEEE 2018.
20.S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, Jun. 2017.
21.N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from RGBD images,” in Proc. Eur. Conf. Comput. Vis. (ECCV), Sep. 2012, pp. 746–760.
22.J. Dai, Y. Li, K. He, and J. Sun, “R-FCN: Object detection via region-based fully convolutional networks,” in Proc. Conf. Neural Inf. Process.Syst. (NIPS), Dec. 2016, pp. 379–387.
23.Jiaming Han, Zhong Yang, Qiuyan Zhang, Cong Chen, Hongchen Li, Shangxiang Lai, Guoxiong Hu, Changliang Xu, Hao Xu, Di Wang and Rui Chen,”A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection”,Appl, Sci, 2019, 9(10), 2009;https://doi.org/10.3390/app9102009.23
24.Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
25.Lin, M.; Chen, Q.; Yan, S. Network in network. arXiv 2013, arXiv:1312.4400.
26.Hao Wang, Guodong Yang , En Li , Yunong Tian, Meng Zhao , Zize Liang; ‘High-Voltage Power Transmission Tower Detection Based on Faster R-CNN and YOLO-V3’, Proceedings of the 38th Chinese Control Conference July 27-30, 2019, Guangzhou, China.
27.Xinyu Liu, Xiren Miao, Hao Jiang, Member, IEEE, Jing Chen.’ Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology’, arXiv:2003.09802v1 [cs.CV] 22 Mar 2020.
28.https://github.com/InsulatorData/InsulatorDataSet.
29.Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille, “weightstandardization”, arXiv:1903.10520, 2019. |
| Description: | 碩士
國立政治大學
資訊科學系
107753023 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107753023 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202101299 |
| Appears in Collections: | [資訊科學系] 學位論文
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