In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primiti.
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To eliminate redundancy among feature embeddings and acquire effective representations of defects in photovoltaic panels, we propose a YOLO-ACF network model equipped with an Adaptive Complementary Fusion (ACF) module. This aims to enhance detection performance, achieve model lightweighting, and accelerate detection speed.
The excellent performance of YOLOv5 in the field of visual detection, along with its successful application in industry defect detection, proves that it would be a good choice as the baseline network for photovoltaic panel defect detection.
To meet the data requirements, Su et al. 18 proposed PVEL-AD dataset for photovoltaic panel defect detection and conducted several subsequent studies 19, 20, 21 based on this dataset. In recent years, the PVEL-AD dataset has become a benchmark for photovoltaic (PV) cell defect detection research using electroluminescence (EL) images.
Buerhop et al. 17 constructed a publicly available dataset using EL images for optical inspection of photovoltaic panels. Based on this dataset, researchers have developed numerous algorithms 9, 10, 12 for photovoltaic panel defect detection.
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How to reduce redundancy among feature embeddings in photovoltaic panels? To eliminate redundancy among feature embeddings and acquire effective representations of defects in photovoltaic
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Detecting defects on photovoltaic panels using electroluminescence images can significantly enhance the production quality of these panels. Nonetheless, in the process of defect
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The proposed method is based on the previously proposed region–line primitive association framework, in which complementary information between region (segment) and line (straight line)
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