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2023-10-25
Improvement and Research on Object Detection Algorithm for Wind Turbine Blade Defect Scenarios Based on YOLOv8
Authors
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Maoyu Zhu
Illinois Institute of Technology, Chicago, IL 60616, USA
DOI:
https://doi.org/10.30564/jcsr.v6i4.7072Abstract
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 automationReferences
[1] Schubel, P.J., Crossley, R.J., 2012. Wind turbine blade design. Energies. 5(9), 3425–3449.
[2] Sohan, M., Sai Ram, T., Reddy, R., et al., 2024. A review on yolov8 and its advancements. In International Conference on Data Intelligence and Cognitive Informatics (pp. 529--545). Singapore: Springer.
[3] Duan, K., Xie, L., Qi, H., et al., 2020. Corner proposal network for anchor-free, two-stage object detection. In European Conference on Computer Vision (pp. 399--416). Cham: Springer International Publishing.
[4] Gao, J., Chen, Y., Wei, Y., et al., 2021. Detection of specific building in remote sensing images using a novel YOLO-S-CIOU model. Case: gas station identification. Sensors. 21(4), 1375.
[5] Zhang, H., Xu, C., Zhang, S., 2023. Inner-IoU: more effective intersection over union loss with auxiliary bounding box. arXiv preprint arXiv:2311.02877.
[6] Chen, Z., Chen, K., Lin, W., et al., 2020. Piou loss: Towards accurate oriented object detection in complex environments. In Computer Vision---ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16 (pp. 195--211). Springer International Publishing.
[7] Sunkara, R., Luo, T., 2022. No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. In Joint European conference on machine learning and knowledge discovery in databases (pp. 443-459). Cham: Springer Nature Switzerland.
[8] Tan, M., Pang, R., Le, Q.V., 2020. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10781--10790).
[9] Liu, Y., Shao, Z., Hoffmann, N., 2021. Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561.
[10] Hendrycks, D., Mazeika, M., Kadavath, S., et al., 2019. Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems, 32.
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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
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Copyright © 2024 Author(s)
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
