Improved YOLOv7-based photovoltaic panel defect detection

Yifeng Sun

doi:10.1117/12.3026359

9 February 2024 Improved YOLOv7-based photovoltaic panel defect detection

Yifeng Sun

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130731N (2024) https://doi.org/10.1117/12.3026359
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

To address the challenges of small defect objects and complex background in photovoltaic panel defect detection, an improved YOLOv7 based photovoltaic panel defect detection is proposed in this paper. Coordinate attention mechanism is incorporated to enhance the model's global perception capabilities. Additionally, C-IoU loss function is adopted to optimize training while ensuring improved training accuracy. Experimental results conducted on public dataset demonstrate that the proposed method outperforms baseline object detection algorithms, achieving a mean Average Precision (mAP) of 93.9%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifeng Sun "Improved YOLOv7-based photovoltaic panel defect detection", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130731N (9 February 2024); https://doi.org/10.1117/12.3026359

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