In photovoltaic (PV) cell inspection, electroluminescence (EL) imaging provides high spatial resolution for detecting various types of defects. The recent integration of EL imaging with deep learning models has enhanced the recognition of defects in PV cells. However, the high surface impurity content in polycrystalline silicon PV cells presents a challenge. Defect features can be similar to complex background textures, making highlighting features for small target defects difficult and leading to potential misclassification as background or other classes. To address these challenges, we propose a novel deep convolutional neural network (CNN) model for effectively identifying small target defects in polycrystalline PV cells. We first utilize a global context information (GCI) block to improve CNN’s modeling of global information, aiding in distinguishing PV cell defects with similar local details. Moreover, we design a channel weight feature pyramid (CWFP) that dynamically adjusts the channel weights of extracted features. We further achieve multi-scale feature fusion through the pyramid structure to enhance the resolution capability for small targets. The proposed model was evaluated on a publicly available dataset of 8 defect classes in polycrystalline PV cells, achieving an accuracy of 96.36 %. The experimental results show that the proposed model outperforms existing deep learning models in detecting small target defects with higher precision.
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