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With the rapid evolution of smart grid technologies, UAV-assisted power equipment inspection has emerged as a critical approach for ensuring operational stability and efficiency in next-generation power systems. Recent advancements in deep learning-driven object detection methodologies have demonstrated promising results for power infrastructure applications, where symmetry in equipment geometry and asymmetry in defect patterns present unique analytical challenges. However, three critical issues persist: (1) the scarcity of annotated datasets creating asymmetry in data distribution, (2) the prevalence of small-scale targets with symmetrical structural features, and (3) the interference from asymmetrical environmental backgrounds – all impacting maintenance effectiveness. This paper systematically surveys global research progress while analyzing the evolution of detection frameworks through the lens of symmetry-aware feature extraction and asymmetry-tolerant modeling. We establish evaluation criteria emphasizing balanced performance in symmetrical component localization and asymmetrical defect characterization. Comparative analyses of leading models reveal how symmetry-enhanced architectures improve small-target recognition, while asymmetry-adaptive mechanisms mitigate complex background interference. Our investigation highlights specialized deep learning implementations for power system applications, including symmetry-preserving component detection and asymmetry-sensitive defect diagnosis. The synthesis identifies key research directions: addressing data asymmetry through synthetic augmentation, enhancing symmetry exploitation in multi-scale detection, and developing hybrid architectures that balance structural symmetry learning with environmental asymmetry suppression. Practical implementation strategies are proposed to advance intelligent inspection capabilities while maintaining methodological alignment with symmetry-asymmetry principles in data analysis .

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A Review of Object Detection Techniques for Novel Power Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Fangyu Zhang,
  • Song Deng,
  • Ziwei Ding,
  • Qingsheng Liu,
  • Zhejie Shen,
  • Lechan Yang

摘要

With the rapid evolution of smart grid technologies, UAV-assisted power equipment inspection has emerged as a critical approach for ensuring operational stability and efficiency in next-generation power systems. Recent advancements in deep learning-driven object detection methodologies have demonstrated promising results for power infrastructure applications, where symmetry in equipment geometry and asymmetry in defect patterns present unique analytical challenges. However, three critical issues persist: (1) the scarcity of annotated datasets creating asymmetry in data distribution, (2) the prevalence of small-scale targets with symmetrical structural features, and (3) the interference from asymmetrical environmental backgrounds – all impacting maintenance effectiveness. This paper systematically surveys global research progress while analyzing the evolution of detection frameworks through the lens of symmetry-aware feature extraction and asymmetry-tolerant modeling. We establish evaluation criteria emphasizing balanced performance in symmetrical component localization and asymmetrical defect characterization. Comparative analyses of leading models reveal how symmetry-enhanced architectures improve small-target recognition, while asymmetry-adaptive mechanisms mitigate complex background interference. Our investigation highlights specialized deep learning implementations for power system applications, including symmetry-preserving component detection and asymmetry-sensitive defect diagnosis. The synthesis identifies key research directions: addressing data asymmetry through synthetic augmentation, enhancing symmetry exploitation in multi-scale detection, and developing hybrid architectures that balance structural symmetry learning with environmental asymmetry suppression. Practical implementation strategies are proposed to advance intelligent inspection capabilities while maintaining methodological alignment with symmetry-asymmetry principles in data analysis .