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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (5): 918-927.DOI: 10.11996/JG.j.2095-302X.2023050918

• Image Processing and Computer Vision • Previous Articles     Next Articles

Multi-fitting detection method for transmission lines based on implicit spatial knowledge fusion

ZHAI Yong-jie1(), GUO Cong-bin1, WANG Qian-ming1, ZHAO Kuan1, BAI Yun-shan1, ZHANG Ji2()   

  1. 1. Department of Automation, North China Electric Power University, Baoding Hebei 071003, China
    2. Department of Computer Science, North China Electric Power University, Baoding Hebei 071003, China
  • Received:2023-04-24 Accepted:2023-08-25 Online:2023-10-31 Published:2023-10-31
  • Contact: ZHANG Ji (1972-), associate professor, Ph.D. His main research interests cover computer vision, image processing and information fusion. E-mail:zhangji@ncepu.edu.cn
  • About author:ZHAI Yong-jie (1972-), professor, Ph.D. His main research interests cover pattern recognition and digital image processing. E-mail:zhaiyongjie@ncepu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(U21A20486);National Natural Science Foundation of China(61871182);Fundamental Research Funds for the Central Universities(2021MS081);Fundamental Research Funds for the Central Universities(2023JC006)


To address the challenge of detecting tiny-size and dense occlusion objects in the task of multi-fitting detection for transmission lines, a transmission line multi-fitting detection method based on implicit spatial knowledge fusion was proposed. First, in order to mine the implicit spatial knowledge among transmission line fittings and assist the model in detection, the spatial box setting module and the spatial context extraction module were proposed to set the spatial box and extract the spatial context information. Then, the spatial context memory module was designed to filter and remember the spatial context information to aid the positioning of multi-fitting detection model. Finally, the post-processing part of the model was enhanced to further mitigate the issue of low detection accuracy stemming from dense occlusion by fittings. The experimental results demonstrated the efficacy of the proposed model in enhancing the detection of various kinds of fittings, especially those of tiny size and dense occlusion. Compared with the baseline model, the AP50 evaluation index and the more stringent AP75 evaluation index were increased by 3.5% and 5.7%, respectively. It laid a foundation for the application of fitting detection and further fault diagnosis.

Key words: transmission line, fitting detection, deep learning, implicit spatial knowledge, spatial context information

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