Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100003-6.doi: 10.11896/jsjkx.240100003

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

PS YOLOv8:Enhancing Detection of Small-scale Damage in Power Lines Inspection

SONG Shangze1, LI Li1, TIAN Ye2, BAI Jie2   

  1. 1 Department of Electrical and Control Engineering,Shanxi Institute of Engineering and Technology,Yangquan,Shanxi 045000,China
    2 Department of Big Data and Intelligent Engineering,Shanxi Institute of Engineering and Technology,Yangquan,Shanxi 045000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:SONG Shangze,born in 2001,undergraduate.His main research interests include electrical engineering and artificial intelligence.
    LI Li,born in 1981,master,associate professor.Her main research interests include electrical engineering and electronic design.
  • Supported by:
    College Student Innovation and Entrepreneurship Training Program Project(202314527003).

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Abstract: In the field of power line inspection,accurately detecting minute cracks and small damages,which are often overlooked due to their scale and background complexity,is crucial.If not identified and addressed timely,these minor damages can escalate into major safety hazards.To address this challenge,this paper introduces the PowerScreen-YOLOv8(PS-YOLOv8) model.Compared to the original YOLOv8,this model has made significant advancements in detecting small objects.It integrates six key improvements to enhance detection accuracy in complex environments.The model's superiority has been demonstrated through rigo-rous testing and benchmarking against leading algorithms.With an impressive accuracy rate of 90.3% and validated robustness in real-world drone-captured scenarios,PS-YOLOv8 represents a significant leap in power line inspection technology.It offers a more reliable,efficient,and safer approach to infrastructure maintenance.

Key words: Power inspection, YOLOv8, Small object detection, Deep learning

CLC Number: 

  • TP391
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