Dong Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Dong Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Hongwei Wang
Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, China
1 shared publication- Junjie Liu
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
1 shared publication Qiang Gao
Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, China
1 shared publication- Xiao Yu
College of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
1 shared publication Yipin Wang
State Grid Tianjin Electric Power Company, Tianjin, China
1 shared publication- Zhaoyang Wang
School of Artificial Intelligence, Hebei University of Technology
1 shared publication
Latest Publications
- Open Access
- 6 April 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 9, 94970-94980
Insulator Anomaly Detection Method Based on Few-Shot Learning
Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.
- 6 April 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 9, 94970-94980
Open AccessInsulator Anomaly Detection Method Based on Few-Shot Learning
Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.