November 30, 2025ComputersOpen Access

Advancing Small Defect Recognition in PV Modules with YOLO-FAD and Dynamic Convolution

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Achieving 94.6% overall accuracy highlights the strength of the YOLO-FAD model for small defect detection in PV modules.
The model's key innovations include improved classification through enhanced feature extraction and network structure modifications.
Assessment through performance metrics indicates robustness, marking a significant advancement over state-of-the-art detection models.
Integration of RFAConv and DyC3K2 ensures effective localization and detection of small defects in complex backgrounds.

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YOLO-FAD enhances defect classification and localization in photovoltaic modules, suggesting improved robustness for small defect detection.

Li et al. (2025) studied this question.

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YOLO-FAD enhances defect classification and localization in photovoltaic modules, suggesting improved robustness for small defect detection.