The invention relates to the technical field of flatness detection of battery boards, in particular to a method and a system for detecting flatness of a photovoltaic board in photovoltaic construction. The problem that the levelness of the assembled photovoltaic panel battery cannot be detected after the glass is packaged. . This paper proposes a lightweight PV defect detection algorithm based on an improved YOLOv11n architecture. Building upon the original YOLOv11n framework, two modules are introduced to enhance model performance: (1) the CFA module (Channel-wise Feature Aggregation), which improves feature. . Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections.
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