mlsanityData to 14 Oct 2026

Deep Learning in Automated Power Line Inspection: A Review

arXiv 2502.07826 ↗First posted 10 Feb 2025cs.CV eess.IVcited by 4

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Abstract (arXiv)

In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of an extensive collection of videos and images has become essential for maintaining the reliability, safety, and sustainability of electricity transmission. A significant focus on applying deep learning techniques for enhancing power line inspection processes has been observed in recent research. A comprehensive review of existing studies has been conducted in this paper, to aid researchers and industries in developing improved deep learning-based systems for analyzing power line data. The conventional steps of data analysis in power line inspections have been examined, and the body of current research has been systematically categorized into two main areas: the detection of components and the diagnosis of faults. A detailed summary of the diverse methods and techniques employed in these areas has been encapsulated, providing insights into their functionality and use cases. Special attention has been given to the exploration of deep learning-based methodologies for the analysis of power line inspection data, with an exposition of their fundamental principles and practical applications. Moreover, a vision for future research directions has been outlined, highlighting the need for advancements such as edge-cloud collaboration, and multi-modal analysis among others. Thus, this paper serves as a comprehensive resource for researchers delving into deep learning for power line analysis, illuminating the extent of current knowledge and the potential areas for future investigation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 159 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

34 of 159 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  6. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  7. 2023Segment Anythingcited by 8,234
  8. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  9. 2020End-to-End Object Detection with Transformerscited by 7,414
  10. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  11. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  12. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
22 more
  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  4. 2018YOLOv3: An Incremental Improvementcited by 2,659
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  6. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  7. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  8. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  10. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  11. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  12. 2020A Survey on Visual Transformercited by 654
  13. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  14. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  15. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
  16. 2016Speed/accuracy trade-offs for modern convolutional object detectorscited by 198
  17. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  18. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  19. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  20. 2020TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Linescited by 28
  21. 2021STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Imagescited by 6
  22. 2020Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Linescited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Comparing Object Detection Models for Electrical Substation Component Mappingcited by 0
  2. 2026GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructurecited by 0
  3. 2026DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platformscited by 0
  4. 2026Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagerycited by 0