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 representation of subtle defects through the integration of context and local pathways, channel. . 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 representation of subtle defects through the integration of context and local pathways, channel. . However, PV panels are prone to various defects such as cracks, micro-cracks, and hot spots during manufacturing, installation, and operation, which can significantly reduce power generation efficiency and shorten equipment lifespan. Therefore, fast and accurate defect detection has become a vital. . 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. To address these issues, this paper proposes the EBBA-Detector.
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