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Transformer Image Quality Assessment Based on Multi-directional Feature Extraction

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15034))

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Abstract

Transformer image quality is influenced by various factors, including acquisition equipment and external environmental conditions. There is an urgent need for a quality evaluation method to assess transformer image quality, particularly to enhance the accuracy of transformer oil leakage identification tasks. Addressing this issue, this paper introduces image quality evaluation to the field of transformers for the first time and proposes a novel model called Multi-Directional Feature Extraction Transformer Image Quality Assessment (MFE-TIQA) for automatic evaluation of large-scale transformer images. MFE-TIQA comprises a main branch and a sub-branch. The main branch utilizes convolutional neural networks to extract multi-scale features from transformer images and employs a multi-directional feature extraction module to capture fine details. Meanwhile, the sub-branch employs a super-pixel segmentation model to generate local visual information from transformer images. Subsequently, a multi-branch information fusion module is constructed to comprehensively integrate the information from both branches, enhancing the model’s focus on critical details. Furthermore, comparative experiments are conducted on a self-constructed transformer image dataset and a publicly available dataset to validate the effectiveness of the proposed model. This research provides foundational evidence for subsequent transformer oil leakage identification and detection efforts.

This work was supported in part by the National Natural Science Foundation of China under Grant 62371188.

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Authors and Affiliations

  1. Department of Computer, North China Electric Power University, Baoding, China

    Wenqing Zhao, Mengwei Li & Yumei Ma

  2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, North China Electric Power University, Baoding, China

    Wenqing Zhao

  3. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding, China

    Yumei Ma

Authors
  1. Wenqing Zhao
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  2. Mengwei Li
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  3. Yumei Ma
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Corresponding author

Correspondence to Wenqing Zhao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Zhao, W., Li, M., Ma, Y. (2025). Transformer Image Quality Assessment Based on Multi-directional Feature Extraction. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15034. Springer, Singapore. https://doi.org/10.1007/978-981-97-8505-6_25

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  • DOI: https://doi.org/10.1007/978-981-97-8505-6_25

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  • Print ISBN: 978-981-97-8504-9

  • Online ISBN: 978-981-97-8505-6

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