[1]ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16(2):237-246.[doi:10.11992/tis.202012023]
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Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 237-246 Column: 学术论文—机器学习 Public date: 2021-03-05
Title:
Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection
Author(s):
ZHAI Yongjie1;  YANG Xu1;  ZHAO Zhenbing2;  WANG Qianming1;  ZHAO Wenqing1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Department, University, School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
transmission lines; fitting; deep learning; object detection; Faster R-CNN; structured assembly; co-occurrence matrix; co-occurrence reasoning module
CLC:
TP18
DOI:
10.11992/tis.202012023
Abstract:
To promote the organic integration of object detection and business knowledge in the electric power field, alleviate the problem of long-tailed distribution among fitting samples, and effectively improve the automatic detection effect of transmission line fittings, we propose a faster region-based convolutional neural network (Faster R-CNN) transmission line fitting detection model based on integrating co-occurrence reasoning. First, the structural combination rule of transmission line fittings is extensively investigated, and the co-occurrence connection relationship between objects is effectively expressed using conditional probability in a data-driven manner. Then, in combination with the graph learning method, the co-occurrence probability association is learned and mapped as the adjacency matrix of the co-occurrence graph, the feature vector extracted from the baseline model (Faster R-CNN) is used as the graph inference input feature, and the self-learning transformation matrix is used as the propagation weight of the co-occurrence graph to complete graph information propagation and realize effective co-occurrence inference model embedding. The experimental results show that the Faster R-CNN integrating co-occurrence reasoning module outperforms the original model by 6.56%, which is particularly significant for performance improvement in terms of transmission line fitting detection with a reduced long-tailed distribution among fitting samples.
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