Isolator Detection in Power Transmission Lines using Lightweight Dept-wise Convolution with BottleneckCSP YOLOv5

Öz

The detection of insulators is of great importance in power transmission lines. This is because accurate detection ensures reliability and continuity of energy transmission, preventing line interruptions. The proposed method in this study utilizes the DWB-YOLOv5 (Dept-wise convolution with BottleneckCSP YOLOv5) model to effectively detect insulators, contributing to the safe and uninterrupted operation of power lines. In the suggested approach, the DWB-YOLOv5 model is employed to detect insulators. The bottleneckCSP module enhances the accuracy of targets at various scales, while the depth-wise c2onvolution module assists in reducing the model's complexity. Images undergo preprocessing steps such as automatic orientation and resizing. The preprocessed images are fed into the DWB-YOLOv5 model to extract deep features, perform object detection, and conduct classification. The insulator detection model obtained through this method exhibits a minimum of 8.53% better mean average precision (mAP) performance compared to existing methods. This study represents a significant step towards ensuring the safe and uninterrupted operation of power transmission lines. Accurate detection of insulators facilitates the smooth functioning of lines, ensuring reliability and continuity in energy transmission. The proposed method offers important advantages such as high accuracy, lightweight design, and efficiency.

Anahtar Kelimeler

• Object Detection
• Yolov5
• BottleneckCSP
• Transmission Line
• Convolution Module

Kaynakça

1. [1] E. B. M. Tayeb and O. A. A. A. Rhim, (2011). Transmission line faults detection, classification and location using artificial neural network. presented at the 2011 International Conference & Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE), pp. 1–5. DOI:10.1109/ICUEPES.2011.6497761
2. [2] E. Karakose, “Performance evaluation of electrical transmission line detection and tracking algorithms based on image processing using UAV,” presented at the 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017, pp. 1–5. DOI:10.1109/IDAP.2017.8090302
3. [3] H. Liang, C. Zuo, and W. Wei, (2020). Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning, IEEE Access, 8;38448–38458. DOI: 10.1109/ACCESS.2020.2974798
4. [4] H. Ha, S. Han, and J. Lee, (2012). Fault Detection on Transmission Lines Using a Microphone Array and an Infrared Thermal Imaging Camera,” IEEE Trans. Instrum. Meas., 61(1);267–275, DOI: 10.1109/TIM.2011.2159322
5. [5] C. Liu, Y. Wu, J. Liu, Z. Sun, and H. Xu, (2021). Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model. Appl. Sci., 11(10) DOI: https://doi.org/10.3390/app11104647
6. [6] H. Jiang, X. Qiu, J. Chen, X. Liu, X. Miao, and S. Zhuang. (2019). Insulator Fault Detection in Aerial Images Based on Ensemble Learning With Multi-Level Perception, IEEE Access, 7;61797–61810. DOI: 10.1109/ACCESS.2019.2915985
7. [7] C. Chen, G. Yuan, H. Zhou, and Y. Ma, (2023). Improved YOLOv5s model for key components detection of power transmission lines. Math. Biosci. Eng., 20(5);7738–7760. DOI: 10.3934/mbe.2023334
8. [8] C. Liu, Y. Tao, J. Liang, K. Li, and Y. Chen, “Object Detection Based on YOLO Network,” presented at the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), 2018, pp. 799–803. DOI:10.1109/ITOEC.2018.8740604

9. [9] N. Al-Qubaydhi, A. Alenezi, T. Alanazi, A. Senyor, N. Alanezi, B. Alotaibi, M. Alotaibi, A. Razaque, A. A. Abdelhamid, and A. Alotaibi, (2022). Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning. Electronics, 11(17); 2669, 2022. DOI: https://doi.org/10.3390/electronics11172669
10. [10] Z. Qiu, X. Zhu, C. Liao, D. Shi, and W. Qu, (2022). Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model,” Appl. Sci., 12,;3 DOI: https://doi.org/10.3390/app12031207
11. [11] X. Wang, W. Li, W. Guo, and K. Cao, “SPB-YOLO: an efficient real-time detector for unmanned aerial vehicle images,” presented at the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2021, pp. 099–104. DOI: https://doi.org/10.3390/drones7030190
12. [12] “Kaggle,” Kaggle data set. [Online]. Available: https://www.kaggle.com/. [Accessed: 10-Dec-2021].
13. [13] E. Hewlett, (1907). new type of insulator for high-tension transmission lines,” Proc. Am. Inst. Electr. Eng., 26(6);975–979.
14. [14] I. Atik, (2023). Parallel Convolutional Neural Networks and Transfer Learning for Classifying Landforms in Satellite Images. Inf. Technol. Control, 52(1);228–244 DOI: https://doi.org/10.5755/j01.itc.52.1.31779
15. [15] I. Atik, (2022). Classification of Electronic Components Based on Convolutional Neural Network Architecture Energies, 15;7 DOI: https://doi.org/10.3390/en15072347
16. [16] I. Atik, (2022). Performance Comparison of Pre-Trained Convolutional Neural Networks in Flower Image Classification, Eur. J. Sci. Technol., 35;315–321 DOI: https://doi.org/10.31590/ejosat.1082023