5 / 2023-08-04 15:34:46
Defect Recognition Method of Digital Twin Power Line Image Based on Improved TridentNet
Neural Networks,Defect Detection,Power Line,TridentNet,Weights normalization
Final Paper
Fan XU / State Grid Information & Telecommunication Group CO,LTD;Beijing Insititute of technology
Yuanyuan LIU / State Grid Information & Telecommunication Group CO,LTD
Fan YANG / Aostar Information Technologies Co.,Ltd
Wenpu LI / State Grid Information & Telecommunication Group CO,LTD
Xiaomeng DI / China Electric Power Research Institute
Xiaodong DU / Hebei Electric Power Research Institute of State Grid
Xingtao WANG / State Grid Information & Telecommunication Group CO,LTD
The detection of defects in Power Line images plays a vital role in timely and effective identification of equipment flaws, thereby preventing equipment failures. Current methods for transmission image defect detection need further algorithmic research to improve their capability to detect defects at different scales. In this paper, we propose an improved method for the TridentNet network specifically designed for transmission line inspection scenarios. We optimize the scale-based training strategy by adjusting the selection range to enhance the model training effectiveness. We introduce a new dimension in the backbone network to improve feature extraction efficiency. Additionally, we design a weight normalization process in the feature extraction part of the network to accelerate model convergence and improve accuracy when training data batches are small. These enhancements contribute to improved accuracy and recall rates for multi-scale defect detection in the same image. Experimental results demonstrate the effectiveness and accuracy of our proposed method.
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University