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A Camera-Based Multimodal Defect Sensing Framework for Substation Equipment Monitoring via Cross-Modal Feature

Ziquan Liu1, Hai Xue1, Chengbo Hu1

  • 1Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
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This study introduces a multimodal sensing framework for substation equipment monitoring, enhancing defect detection by integrating images, text, and topology data. The novel approach improves accuracy and reduces errors in complex inspection environments.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Electrical Engineering

Background:

  • Vision-only defect detection faces limitations in complex substation inspections, including image-semantic misalignment and spatial-logic conflicts.
  • Existing methods struggle to effectively integrate diverse data sources like images, text, and equipment topology.

Purpose of the Study:

  • To propose a camera-sensor-based multimodal defect sensing framework for substation equipment monitoring.
  • To address limitations in vision-only defect detection and improve accuracy in complex scenarios.

Main Methods:

  • A unified workflow involving domain-adaptive pre-training, bidirectional cross-modal feature mapping, and hierarchical neuro-symbolic reasoning.
  • Utilized Contrastive Language-Image Pre-training (CLIP) for enhanced semantic representation.
Keywords:
advanced sensing technologycross-modal feature mappingintelligent inspectionmultimodal perceptionneuro-symbolic reasoningsubstation equipment monitoring

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  • Developed a cross-modal mapping network with uncertainty-aware fusion and prototype constraints, coupled with a neuro-symbolic module for reasoning.

    • Main Results:

    • Achieved 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score on a substation inspection dataset.
    • Demonstrated superior performance compared to mainstream and recent defect detection models.
    • Successfully integrated visual, textual, and topological data for improved defect sensing.

    Conclusions:

    • The proposed multimodal framework significantly enhances defect detection accuracy and reliability in substation equipment monitoring.
    • The integration of cross-modal mapping and hierarchical reasoning effectively overcomes limitations of traditional vision-based methods.
    • This approach offers a robust solution for complex industrial inspection tasks.