[1]ZHAI Yongjie,WANG Qianming,YANG Xu,et al.A multi-fitting decoupling detection method for transmission lines based on external knowledge[J].CAAI Transactions on Intelligent Systems,2022,17(5):980-989.[doi:10.11992/tis.202107026]
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A multi-fitting decoupling detection method for transmission lines based on external knowledge

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 980-989 Column: 学术论文—智能系统 Public date: 2022-09-05
Title:
A multi-fitting decoupling detection method for transmission lines based on external knowledge
Author(s):
ZHAI Yongjie1;  WANG Qianming1;  YANG Xu1;  ZHAO Zhenbing2;  ZHAO Wenqing1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
transmission line; fitting; deep learning; object detection; co-occurrence knowledge; spatial knowledge; knowledge reasoning module; decoupling detection
CLC:
TP18
DOI:
10.11992/tis.202107026
Abstract:
This paper proposes a multi-object decoupling detection method based on external knowledge (EKD R-CNN) to effectively solve the problems of object density and mutual occlusion in the process of multi-fitting detection of transmission lines. First, the domain rules and image information of the fittings datasets are deeply analyzed to extract the co-occurrence and spatial knowledge; then, graph neural network methods are used to build co-occurrence and spatial knowledge reasoning models, to instantiate and express external knowledge; finally, the decoupling module is employed to train and learn the fittings detection task in an uncoupled way. Multiple qualitative and quantitative experiments are conducted on datasets with 14 types of fittings in the experiment phase. The contrast experiment shows that the detection effect of EKD R-CNN is better than that of other advanced object detection models and that compared with the original baseline model, the detection accuracy of the algorithm is increased by 6.6%; the qualitative experiments suggest that the proposed algorithm can solve the problem of object occlusion, and the ablation experiment indicates that each module improves the detection effect of the model to a certain extent.
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