[1] Alkire, S., Conconi, A., Robles, G. and Vaz, A. (2015). ‘Destitution: Who and where are the poorest of the poor?’, OPHI Briefing 35, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

[2] S. Kufeoglu, *Economic Impacts of Electric Power Outages and Evaluation of Customer Interruption Costs*. Doctoral Dissertation in Permission of Aalto University, School of Electrical Engineering. 2015; 1-64.

[3] M. P. Blimpo and M. Cosgrove-Davies, *Electricity Access in Sub-Saharan Africa: Uptake, Reliability, and Complementary Factors for Economic Impact*. Africa Development Forum Washington, D.C. World Bank Group (2019).

[4] T. Ayodele, A. Ogunjuyigbe, and O. Oladele, “Improving the Transient Stability of Nigerian 330Kv Transmission Network Using Static Var Compensation Part I: the Base Study,” *Niger. J. Technol.*, vol. 35, no. 1, p. 155, 2015, doi: 10.4314/njt.v35i1.23.

[5] P. Bertheau, C. Cader, and P. Blechinger, “Electrification modelling for Nigeria,” *Energy Procedia*, vol. 93, no. March, pp. 108–112, 2016, doi: 10.1016/j.egypro.2016.07.157.

[6] L. Matikainen *et al.*, “Remote sensing methods for power line corridor surveys,” *ISPRS J. Photogramm. Remote Sens.*, vol. 119, pp. 10–31, 2016, doi: 10.1016/j.isprsjprs.2016.04.011.

[7] Z. Xue, S. Luo, Y. Chen, and L. Tong, “The application of the landslides detection method based on sar images to transmission line corridor area,” *2016 13th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. ICCWAMTIP 2017*, pp. 163–166, 2017, doi: 10.1109/ICCWAMTIP.2016.8079829.

[8] L. Yan, W. Wu, and T. Li, “Power transmission tower monitoring technology based on TerraSAR-X products,” *Int. **Symp. Lidar Radar Mapp. 2011 Technol. Appl.*, vol. 8286, p. 82861E, 2011, doi: 10.1117/12.912336.

[9] L. F. Luque-Vega, B. Castillo-Toledo, A. Loukianov, and L. E. Gonzalez-Jimenez, “Power line inspection via an unmanned aerial system based on the quadrotor helicopter,” *Proc. Mediterr. Electrotech. Conf. - MELECON*, no. April, pp. 393–397, 2014, doi: 10.1109/MELCON.2014.6820566.

[10] M. Wang, W. Tong, and S. Liu, “Fault detection for power line based on convolution neural network,” *ACM Int. Conf. Proceeding Ser.*, vol. Part F1285, pp. 95–101, 2017, doi: 10.1145/3094243.3094254.

[11] H. Guan, Y. Yu, J. Li, Z. Ji, and Q. Zhang, “Extraction of power-transmission lines from vehicle-borne lidar data,” *Int. J. Remote Sens.*, vol. 37, no. 1, pp. 229–247, 2016, doi: 10.1080/01431161.2015.1125549.

[12] J. Ahmad, A. S. Malik, M. F. Abdullah, N. Kamel, and L. Xia, “A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera,” *Pattern Anal. Appl.*, vol. 18, no. 2, pp. 419–440, 2015, doi: 10.1007/s10044-014-0391-9.

[13] X. Yu *et al.*, “Comparison of laser and stereo optical, SAR and InSAR point clouds from air- and space-borne sources in the retrieval of forest inventory attributes,” *Remote Sens.*, vol. 7, no. 12, pp. 15933–15954, 2015, doi: 10.3390/rs71215809.

[14] M. Jaya Bharata Reddy, B. Karthik Chandra, and D. K. Mohanta, “Condition monitoring of 11 kV distribution system insulators incorporating complex imagery using combined DOST-SVM approach,” *IEEE Trans. Dielectr. Electr. Insul.*, vol. 20, no. 2, pp. 664–674, 2013, doi: 10.1109/TDEI.2013.6508770.

[15] J. Jiang, L. Zhao, J. Wang, Y. Liu, M. Tang, and Z. Ji, “The electrified insulator paramater measurement for flashover based on photogrammetric method,” *MIPPR 2011 Multispectral Image Acquis. Process. Anal.*, vol. 8002, no. 86, p. 80021I, 2011, doi: 10.1117/12.902054.

[16] Y. Hu and K. Liu, Inspection and Monitoring Technologies of Transmission Lines with Remote Sensing, New York, NY, USA:Academic, pp. 257-264, 2017..

[17] A. Zormpas, K. Moirogiorgou, K. Kalaitzakis, G. A. Plokamakis, and P. Partsinevelos, “Power Transmission Lines Inspection using Properly Equipped Unmanned Aerial Vehicle ( UAV ),” *2018 IEEE Int. Conf. Imaging Syst. Tech.*, pp. 1–5, 2018.

[18] X. Liu, X. Miao, H. Jiang, and J. Chen, “Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology,” pp. 1–29, 2020, doi: 10.1016/j.arcontrol.2020.09.002.

[19] X. Yu, X. Wu, C. Luo, and P. Ren, “Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework,” *GIScience Remote Sens.*, vol. 54, no. 5, pp. 741–758, 2017, doi: 10.1080/15481603.2017.1323377.

[20] Y. Baştanlar and M. Özuysal, “Introduction to machine learning,” *Methods Mol. Biol.*, vol. 1107, pp. 105–128, 2014, doi: 10.1007/978-1-62703-748-8_7.

[21] Z. A. Siddiqui and U. Park, “A Drone Based Transmission Line Components Inspection System with Deep Learning Technique,” *Energies*, vol. 13, no. 13, pp. 1–24, 2020, doi: 10.3390/en13133348.

[22] J. Han *et al.*, “Search like an eagle: A cascaded model for insulator missing faults detection in aerial images,” *Energies*, vol. 13, no. 3, pp. 1–20, 2020, doi: 10.3390/en13030713.

[23] Y. Song *et al.*, “A vision-based method for the broken spacer detection,” *2015 IEEE Int. Conf. Cyber Technol. Autom. Control Intell. Syst. IEEE-CYBER 2015*, pp. 715–719, 2015, doi: 10.1109/CYBER.2015.7288029.

[24] Y. Zhai, H. Cheng, R. Chen, Q. Yang, and X. Li, “Multi-saliency aggregation-based approach for insulator flashover fault detection using aerial images,” *Energies*, vol. 11, no. 2, pp. 1–12, 2018, doi: 10.3390/en11020340.

[25] Y. Zhai, D. Wang, M. Zhang, J. Wang, and F. Guo, “Fault detection of insulator based on saliency and adaptive morphology,” *Multimed. Tools Appl.*, vol. 76, no. 9, pp. 12051–12064, 2017, doi: 10.1007/s11042-016-3981-2.

[26] J. Han *et al.*, “A method of insulator faults detection in aerial images for high-voltage transmission lines inspection,” *Appl. Sci.*, vol. 9, no. 10, pp. 1–22, 2019, doi: 10.3390/app9102009.

[27] Y. Liu *et al.*, “2016 4th International Conference on Applied Robotics for the Power Industry, CARPI 2016,” *2016 4th Int. Conf. Appl. Robot. Power Ind. CARPI 2016*, pp. 1–5, 2016.

[28] J. Fu, G. Shao, L. Wu, L. Liu, and Z. Ji, “Defect detection of line facility using hierarchical model with learning algorithm,” *High Volt. Eng. Dep. CEPRI*, vol. 43, no. 1, pp. 266–275, 2017, doi: 10.13336/j.1003-6520.hve.20161227035.

[29] T. Mao *et al.*, “Defect recognition method based on HOG and SVM for drone inspection images of power transmission line,” *2019 Int. Conf. High Perform. Big Data Intell. Syst. HPBD IS 2019*, no. 61701404, pp. 254–257, 2019, doi: 10.1109/HPBDIS.2019.8735466.

[30] Z. Zhao, G. Xu, Y. Qi, N. Liu, and T. Zhang, “Multi-patch deep features for power line insulator status classification from aerial images,” *Proc. Int. Jt. Conf. Neural Networks*, vol. 2016-Octob, pp. 3187–3194, 2016, doi: 10.1109/IJCNN.2016.7727606.

[31] X. Liu, H. Jiang, J. Chen, J. Chen, S. Zhuang, and X. Miao, “Insulator Detection in Aerial Images Based on Faster Regions with Convolutional Neural Network,” *IEEE Int. Conf. Control Autom. ICCA*, vol. 2018-June, pp. 1082–1086, 2018, doi: 10.1109/ICCA.2018.8444172.

[32] H. Jiang, X. Qiu, J. Chen, X. Liu, X. Miao, and S. Zhuang, “Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-Level Perception,” *IEEE Access*, vol. 7, pp. 61797–61810, 2019, doi: 10.1109/ACCESS.2019.2915985.

[33] X. Tao, D. Zhang, Z. Wang, X. Liu, H. Zhang, and D. Xu, “Detection of power line insulator defects using aerial images analyzed with convolutional neural networks,” *IEEE Trans. Syst. Man, Cybern. Syst.*, vol. 50, no. 4, pp. 1486–1498, 2020, doi: 10.1109/TSMC.2018.2871750.

[34] L. Ma, C. Xu, G. Zuo, B. Bo, and F. Tao, “Detection Method of Insulator Based on Faster R-CNN,” *2017 IEEE 7th Annu. Int. Conf. CYBER Technol. Autom. Control. Intell. Syst. CYBER 2017*, pp. 1410–1414, 2018, doi: 10.1109/CYBER.2017.8446155.

[35] R. Bai, H. Cao, Y. Yu, F. Wang, W. Dang, and Z. Chu, “Insulator Fault Recognition Based on Spatial Pyramid Pooling Networks with Transfer Learning (Match 2018),” *ICARM 2018 - 2018 3rd Int. Conf. Adv. Robot. Mechatronics*, pp. 824–828, 2019, doi: 10.1109/ICARM.2018.8610720.

[36] T. Olawale and S. A. Ad, “REPORT ON STUDENT INDUSTRIAL WORK EXPERIENCE SCHEME ( SIWES ) AT Transmission Company of Nigeria ( TCN ) for the Student Industrial Work Experience Scheme ( SIWES ),” no. August 2018, 2018, doi: 10.13140/RG.2.2.35067.05929.

[37] Y. Hu and K. Liu and C. Mengqi 2019 Free vibration analysis of transmission lines based on the dynamic stiffness methodR. Soc. open sci.6181354181354.

[38] C. Francois, *Deep Learning with Python*, vol. 53, no. 9. 2019.

[39] D. of C. S. U. Stanford, “CS231n: Convolutional Neural Networks for Visual Recognition,” *STANFORD VISION AND LEARNING LAB*. https://cs231n.github.io/convolutional-networks/ (accessed Nov. 21, 2020).

[40] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Handbook of approximation algorithms and metaheuristics,” *ImageNet Classif. with Deep Convolutional Neural Networks*, pp. 1–1432, 2012, doi: 10.1201/9781420010749.

[41] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” *3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc.*, pp. 1–14, 2015.

[42] J. W. Yun, “Deep Residual Learning for Image Recognition arXiv:1512.03385v1,” *Enzyme Microb. Technol.*, vol. 19, no. 2, pp. 107–117, 2015, [Online]. Available: https://arxiv.org/pdf/1512.03385.pdf.

[43] A. Rosebrock, “ImageNet: VGGNet, ResNet, Inception, and Xception with Keras,” *pyimagesearch*, 2017. https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/ (accessed Nov. 25, 2020).

[44] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861, 2017.

[45] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN2015,” *Biol. Conserv.*, vol. 158, pp. 196–204, 2015.

[46] J. Dai, Y. Li, K. He, and J. Sun, “R-FCN: Object Detection via Region-based Fully Convolutional Networks. arXiv:1605.06409, Top of Form

2016.

[47] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” *Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.*, vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.

[48] W. Liu *et al.*, “SSD Net,” *Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)*, vol. 9905 LNCS, pp. 21–37, 2015.

[49] J. Huang *et al.*, “Speed/accuracy trade-offs for modern convolutional object detectors,” *Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017*, vol. 2017-Janua, pp. 3296–3305, 2017, doi: 10.1109/CVPR.2017.351.

[50] W. Shi, S. Bao, and D. Tan, “FFESSD: An accurate and efficient single-shot detector for target detection,” *Appl. Sci.*, vol. 9, no. 20, 2019, doi: 10.3390/app9204276.

[51] K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015, [Online]. Available: http://arxiv.org/abs/1511.08458.

[52] S. Saha, “A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way,” *Medium*, 2018. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53 (accessed Nov. 15, 2020).

[53] N. K. Manaswi, “Deep Learning with Applications Using Python,” *Deep Learn. with Appl. Using Python*, pp. 91–96, 2018, doi: 10.1007/978-1-4842-3516-4.

[54] R. Shanmugamani, A. G. A. Rahman, S. M. Moore, and N. Koganti, *Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using Tensorflow and Keras.*, 1st ed. Birmingham, UK: Packt Publishing Ltd, 2018.

[55] Y. Yang, L. Wang, Y. Wang, and X. Mei, “Insulator self-shattering detection: a deep convolutional neural network approach,” *Multimed. Tools Appl.*, vol. 78, no. 8, pp. 10097–10112, 2019, doi: 10.1007/s11042-018-6610-4.

[56] Arabi S, Haghighat A, Sharma A (2020) A deep-learning-based computer vision solution for construction vehicle detection. Comput Aided Civil Infrastruct Eng 35:753–767.

[57] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” *32nd Int. Conf. Mach. Learn. ICML 2015*, vol. 1, pp. 448–456, 2015.

[58] G. Perin and S. Picek, “On the Influence of Optimizers in Deep Learning-based Side-channel Analysis,” *Cryptol. ePrint Arch.*, no. Report 2020/977, pp. 1–22, 2020, [Online]. Available: https://eprint.iacr.org/2020/977.

[59] O. M. Komolafe and K. M. Udofia, “Review of electrical energy losses in Nigeria,” *Niger. J. Technol.*, vol. 39, no. 1, pp. 246–254, 2020, doi: 10.4314/njt.v39i1.28.