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References

1 
Girshick Ross, Donahue Jeff, Darrell Trevor, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5), arXiv: 1311.2524v5, pp. 1-21Google Search
2 
Ren Shaoqing, He Kaiming, Girshick Ross, 2015, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems, Vol. 28, pp. 1-0Google Search
3 
Redmon Joseph, 2018, YOLOv3: An Incremental Improvement, arXiv:1804.02767v1, pp. 1-6Google Search
4 
Huang Zhanchao, Wang Jianlin, Fu Xuesong, Yu Tao, Guo Yongqi, 2020, DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection, Information Sciences, Vol. 522, pp. 241-258DOI
5 
W. David, T. Yufei, Y. Jun, L. Zhuo, Aug 2018, Deep Learning- Aided Cyber-Attack Detection in Power Transmission Systems, IEEE Power and Energy Society General Meeting (PESGM), USA, pp. 1-5DOI
6 
P. Avagaddi, J. Belwin Edward, K. Ravi, 2018, A review on fault classification methodologies in power transmission systems: Part-1, Journal of Electrical Systems and Infor- mation Technology, Vol. 5, pp. 48-60DOI
7 
P. Joon-Young, K.Seok-Tae, L. Jae-Kyung, H. Ji-Wan, O. Ki-Young, 2019, Automatic Inspection Drone with Deep Learning- based Auto-tracking Camera Gimbal to Detect Defects in Power, ICVISP 2019, No. 46, pp. 4-5DOI
8 
Soltani, Mostafa Mohammad, Zhu Zhenhua, 2016, Automated annotation for visual recognition of construction resources using synthetic images, Automation in Construction, Vol. 62, pp. 14-23DOI
9 
Mayer, Nikolaus, 2018, What makes good synthetic training data for learning disparity and optical flow estimation?, International Journal of Computer Vision, Vol. 126, No. 9, pp. 942-960DOI
10 
Ekbatani, Keivan Hadi, Pujol Oriol, 2017, Synthetic Data Generation for Deep Learning in Counting Pedestrians, ICPRAMDOI
11 
Barbosa, Barros Igor, 2018, Looking beyond appearances: Synthetic training data for deep cnns in re-identification, Computer Vision and Image Understanding, Vol. 167, pp. 50-62DOI
12 
N. Jung, , Image Acquision Appartus for The Image Machine Learning of Distributed Equirement, Korea Patent (No-2017- 0125110).Google Search
13 
N. Jung, , Apparatus and Method for Learning Facilities Using Video File, Korea Patent (No-2018-0073872).Google Search
14 
N. Jung, , Apparatus for dividing, tagging an image and for detecting defect of facilities using the same, Korea Patent (No-2019-0119492).Google Search
15 
Qiankun Ye, 2017, Harbor Detection in Large-Scale Remote Sensing Images Using Both Deep-Learned and Topological Structure Features, 2017 10th International Symposium on Computational Intelligence and Design (ISCID),IEEE, Vol. 1DOI
16 
https://ai.stanford.edu/~jkrause/cars/car_dataset.htmlGoogle Search
17 
http://cocodataset.org/Google Search
18 
http://host.robots.ox.ac.uk/pascal/VOC/Google Search
19 
http://cvrr.ucsd.edu/LISA/lisa-traffic-sign-dataset.htmlGoogle Search
20 
https://medusa.fit.vutbr.cz/traffic/datasets/Google Search
21 
http://www.cvlibs.net/datasets/kitti/Google Search
22 
http://labelme.csail.mit.edu/Google Search
23 
https://www.microsoft.com/en-us/research/project/image-understanding/Google Search
24 
https://groups.csail.mit.edu/vision/datasets/ADE20K/Google Search
25 
https://storage.googleapis.com/openimages/web/index.htmlGoogle Search
26 
Liang-Chieh. Chen, 2018, Encoder-decoder with atrous separable convolution for semantic image segmentation, Proceedings of the European Conference on Computer Vision (ECCV)Google Search
27 
Yoo Jaejun, Ahn Namhyuk, Sohn Kyung-Ah, , Rethin- king Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy, arXiv pre- print arXiv:2004.00448 (2020).Google Search