A Light Weight Mobile Net SSD Algorithm based Identification and Detection of Multiple Defects in Ceramic Insulators
DOI:
https://doi.org/10.71426/jmt.v1.i1.pp59-74Keywords:
Environmental Conditions, Transmission line inspection, Potential defects, Mobile Net SSD.Abstract
Presently, transmission lines play a major role for transferring electricity from generating stations to consumer premises. As the components are exposed to environmental conditions, damage to these components is more severe. Among those components insulators area accountable for 81.3% of line interruptions. In this paper, a drone-based transmission line inspection using mobile net single shot detection (SSD) is proposed that aims to detect the potential defects of insulators. The proposed system employs a regression-based algorithm using mobile net uses an upgraded compact mobile net SSD to identify targets network, allowing for precise categorization and placement of electrical equipment. Simulation is performed by loading the datasets that are collected. There are totally 800 images of ceramic insulators these are sub divided into 3 classes train class has images with 590 test class with 130 and validation (Val) class with 80. In this methodology, mobile net SSD can identify and detect the ceramic insulators with pollution flashover, corroded and broken. Precision, recall, F1 Score are calculated. Analysis is carried out on the variation of batch parameters, size etc. to ascertain the effectiveness of the proposed method.
References
[1] Wang B, Ma F, Ge L, Ma H, Wang H. Icing-EdgeNet: A pruning lightweight edge intelligent method of discriminative driving channel for ice thickness of transmission lines. IEEE Transactions on Instrumentation and Measurement. 2020;70:1–2. Available from: https://doi.org/10.1109/TIM.2020.3018831
[2] Zhao Z, Qi H, Qi Y, Zhang K, Zhai Y, Zhao W. Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines. IEEE Transactions on Instrumentation and Measurement. 2020;69:6080–6091. Available from: https://doi.org/10.1109/TIM.2020.2969057
[3] Davari N, Akbarizadeh G, Mashhour E. Intelligent diagnosis of incipient fault in power distribution lines based on corona detection in UV-visible videos. IEEE Transactions on Power Delivery. 2020;36:3640–3648. Available from: https://doi.org/10.1109/TPWRD.2020.3046161
[4] Rong S, He L, Du L, Li Z, Yu S. Intelligent detection of vegetation encroachment of power lines with advanced stereovision. IEEE Transactions on Power Delivery. 2020 Dec 21;36(6):3477–3485. Available from: https://doi.org/10.1109/TPWRD.2020.3043433
[5] Feng Z, Guo L, Huang D, Li R. Electrical insulator defects detection method based on YOLOv5. In: 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS); 2021 May 14. p. 979–984. IEEE. Available from: https://doi.org/10.1109/DDCLS52934.2021.9455519
[6] Liu C, Wu Y, Liu J, Han J. MTI-YOLO: A lightweight and real-time deep neural network for insulator detection in complex aerial images. Energies. 2021 Mar 5;14(5):1426. Available from: https://doi.org/10.3390/en14051426
[7] Wu C, Ma X, Kong X, Zhu H. Research on insulator defect detection algorithm of transmission line based on CenterNet. PLOS ONE. 2021 Jul 29;16(7):e0255135. Available from: https://doi.org/10.1371/journal.pone.0255135
[8] Qiu Z, Zhu X, Liao C, Shi D, Qu W. Detection of transmission line insulator defects based on an improved lightweight YOLOv4 model. Applied Sciences. 2022 Jan 24;12(3):1207. Available from: https://doi.org/10.3390/app12031207
[9] Tao X, Zhang D, Wang Z, Liu X, Zhang H, Xu D. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018 Oct 15;50(4):1486–1498. Available from: https://doi.org/10.1109/TSMC.2018.2871750
[10] Wang S, Liu Y, Qing Y, Wang C, Lan T, Yao R. Detection of insulator defects with improved ResNeSt and region proposal network. IEEE Access. 2020 Oct 9;8:184841–184850. Available from: https://doi.org/10.1109/ACCESS.2020.3029857
[11] Cheng HY, Zhai YJ, Chen R, et al. Self-shattering defect detection of glass insulators based on spatial features. Energies. 2019;12:543. Available from: https://doi.org/10.3390/en12030543
[12] Zuo D, Hu H, Qian R, Liu Z. An insulator defect detection algorithm based on computer vision. In: 2017 IEEE International Conference on Information and Automation (ICIA); 2017 Jul 18. p. 361–365. IEEE. Available from: https://doi.org/10.1109/ICInfA.2017.8078934
[13] Liu Y, Shao Z, Teng Y, Hoffmann N. NAM: Normalization-based attention module. arXiv. 2021. Available from: https://arxiv.org/abs/2111.12419
[14] Rao Y, Zhao W, Tang Y, Zhou J, Lim SN, Lu J. HorNet: Efficient high-order spatial interactions with recursive gated convolutions. arXiv. 2022. Available from: https://arxiv.org/abs/2207.14284
[15] Gao J, Chen X, Lin D. Insulator defect detection based on improved YOLOv5. In: 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT); 2021 Oct 29. p. 53–58. IEEE. Available from: https://doi.org/10.1109/ACAIT53529.2021.9731270
[16] Biswas S, Parhi V, Anudeep B, Nayak PK. S-transform assisted CUSUM-based protection strategy for transmission lines possessing UPFC. In: 2020 International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET); 2020 Jul 10. p. 1–6. IEEE. Available from: https://doi.org/10.1109/ICEFEET49149.2020.9186956
[17] Anudeep B, Nayak PK. Transient energy-based combined fault detector and faulted phase selector for distribution networks with distributed generators. International Transactions on Electrical Energy Systems. 2020 Apr;30(4):e12288. Available from: https://doi.org/10.1002/2050-7038.12288
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Copyright (c) 2024 Pulla Bhavya Sree, K. Balaji, Naveen Banoth, Mohammad Parhamfar (Author)
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