Improvement and Research on Object Detection Algorithm for Wind Turbine Blade Defect Scenarios Based on YOLOv8

Authors

  • Maoyu Zhu

    Illinois Institute of Technology, Chicago, IL 60616, USA

DOI:

https://doi.org/10.30564/jcsr.v6i4.7072
Received: 19 August 2024 | Revised: 31 August 2024 | Accepted: 3 September 2024 | Published Online: 26 September 2024

Abstract

Wind turbine blades are vital for energy generation, where defects can cause efficiency loss and costly maintenance. This paper proposes an improved object detection algorithm based on YOLOv8 for detecting defects in wind turbine blades. Enhancements include network architecture modifications and advanced attention mechanisms, which boost detection accuracy while maintaining real-time processing. Our approach is tested on a custom dataset, showing better performance than the standard YOLOv8 model. These improvements can enhance automated defect detection in wind turbines, reducing downtime and operational costs, and contributing to more efficient renewable energy maintenance.

Keywords:

Wind turbine blade defect; Object detection; YOLOv8; Algorithm improvement; Attention mechanisms; Renewable energy maintenance; Real-time processing; Defect detection automation

References

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How to Cite

Zhu, M. (2024). Improvement and Research on Object Detection Algorithm for Wind Turbine Blade Defect Scenarios Based on YOLOv8. Journal of Computer Science Research, 6(4), 39–50. https://doi.org/10.30564/jcsr.v6i4.7072