Insulators and Defect Detection Based on the Improved Focal Loss Function
Source abstract
Abstract
Unmanned aerial vehicle (UAV) inspection has become the mainstream of transmission line inspection, and the detection of insulator defects is an important part of UAV inspection. On the premise of ensuring high accuracy and detection speed, an improved YOLOv5 model is proposed for defect detection of insulators. The algorithm uses the weights trained on conventional large-scale datasets to improve accuracy through the transfer learning method of feature mapping. The algorithm employs the Focal loss function and proposes a dynamic weight assignment method. Compared with the traditional empirical value method, it is more in line with the distribution law of samples in the data set, improves the accuracy of difficult-to-classify samples, and saves a lot of time. The experimental results show that the average accuracy of the insulator and its defect is 98.3%, 5.7% higher than the original model, while the accuracy and recall rate of insulator defects are improved by 5.7% and 7.9%, respectively. The algorithm improves the accuracy and recall of the model and enables faster detection of insulator defects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power Line Inspection Robots · Advanced Neural Network Applications · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310000, China
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310000, China
State Grid Taizhou Electric Power Supply Company, Taizhou 318000, China
Funding
the National Natural Science Foundation of China · the Fundamental Research Funds for the Provincial Universities of Zhejiang
References (29)
Show source-provided references
1. A Review on State-of-the-Art Power Line Inspection Techniques10.1109/TIM.2020.3031194 ↗
2. Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning10.1016/j.ijepes.2017.12.016 ↗
3. Faster r-cnn: Towards real-time object detection with region proposal networks
4. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
5. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
6. Multi-stage deep learning networks for automated assessment of electricity transmission infrastructure using fly-by images10.1016/j.epsr.2022.107948 ↗
7. Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks10.1109/TSG.2020.2970156 ↗
8. Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning10.1109/ACCESS.2020.2974798 ↗
9. Wanguo, W., Zhenli, W., Bin, L., Yuechen, Y., and Xiaobin, S. (2019, January 22–24). Typical Defect Detection Technology of Transmission Line Based on Deep Learning. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.10.1109/CAC48633.2019.8996643 ↗
10. Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines10.1109/TIM.2020.2969057 ↗
11. Qiu, Z., Zhu, X., Liao, C., Shi, D., and Qu, W. (2022). Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model. Appl. Sci., 12.10.3390/app12031207 ↗
12. Zhang, Z., Huang, S., Li, Y., Li, H., and Hao, H. (2022). Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning. Energies, 15.10.3390/en15072465 ↗
13. Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27–30). Training Region-Based Object Detectors with Online Hard Example Mining. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.89 ↗
14. Li, M., Zhang, Z., Yu, H., Chen, X., and Li, D. S-OHEM: Stratified online hard example mining for object detection. Proceedings of the CCF Chinese Conference on Computer Vision, Tianjin, China.
15. Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.324 ↗
16. Li, B., Liu, Y., and Wang, X. (February, January 27). Gradient harmonized single-stage detector. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
17. Li, Y., and Liu, L. (2021, January 4–6). YOLO-ResNet: A New Model for Rebar Detection. Proceedings of the 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021, Wuhan, China.10.1109/RCAE53607.2021.9638876 ↗
18. Li, J., Yan, D., Luan, K., Li, Z., and Liang, H. (2020). Deep Learning-Based Bird’s Nest Detection on Transmission Lines Using UAV Imagery. Appl. Sci., 10.10.3390/app10186147 ↗
19. Li, Y., Shi, W., Liu, G., Jiao, L., Ma, Z., and Wei, L. (2021, January 12–16). Sar image object detection based on improved cross-entropy loss function with the attention of hard samples. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium.10.1109/IGARSS47720.2021.9554061 ↗
20. An Industrial-Grade Solution for Crop Disease Image Detection Tasks10.3389/fpls.2022.921057 ↗
21. Cao, C.-Y., Zheng, J.-C., Huang, Y.-Q., Liu, J., and Yang, C.-F. (2019). Investigation of a Promoted You Only Look Once Algorithm and Its Application in Traffic Flow Monitoring. Appl. Sci., 9.10.3390/app9173619 ↗
22. Liu, C., Wu, Y., Liu, J., Sun, Z., and Xu, H. (2021). Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model. Appl. Sci., 11.10.3390/app11104647 ↗
23. (2020, June 25). Ultralytics/yolov5. Available online: https://github.com/ultralytics/yolov5.
24. Wang, C.Y., Liao, H., Wu, Y.H., Chen, P.Y., and Yeh, I.H. (2020, January 14–19). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
25. Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
26. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–23). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
27. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
28. Neubeck, A., and Gool, L. (2006, January 20–24). Efficient Non-Maximum Suppression. Proceedings of the International Conference on Pattern Recognition, Hong Kong, China.10.1109/ICPR.2006.479 ↗
29. Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., and Bailey, J. (November, January 27). Symmetric Cross Entropy for Robust Learning with Noisy Labels. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea.