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September 5, 2025Scientific journal of intelligent systems research.Open Access

Photovoltaic Panel Defect Detection Algorithm based on Improved YOLOv8s

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Authors

ZSZiqi SongPowerChina (China)YYYao YaoMinistry of Education of the People's Republic of China JYJingbin YangRolls-Royce (United Kingdom)

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Implication

Algorithm improves defect detection accuracy to 94.2% in photovoltaic panels, highlighting advancements in dynamic convolution and loss function integration.

Key Points

The improved YOLOv8s algorithm increases defect detection accuracy, particularly for small targets.
The mAP metric improved significantly from 90.6% to 94.2%, showcasing enhanced detection capabilities.
Dynamic convolution and TripletAttention mechanisms are critical components enhancing defect identification.
The proposed method reduces computation to 8.8GFLOPs, making it suitable for efficient real-time applications.

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Song et al. (2025) studied this question.

synapsesocial.com/papers/68bb3d552b87ece8dc955f14 https://doi.org/10.54691/5fdg3j51

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Implication

Algorithm improves defect detection accuracy to 94.2% in photovoltaic panels, highlighting advancements in dynamic convolution and loss function integration.

Photovoltaic Panel Defect Detection Algorithm based on Improved YOLOv8s

Discussion

Member takes

Implication

Key Points

Cite This Study

Photovoltaic Panel Defect Detection Algorithm based on Improved YOLOv8s

Discussion

Member takes

Implication

Key Points

Cite This Study

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