首页|A Multi-scale neighbourhood feature interaction network for photovoltaic cell defect detection

A Multi-scale neighbourhood feature interaction network for photovoltaic cell defect detection

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier B.V.Photovoltaic power generation is a critical component in the industrial sector, with the efficiency of energy production being influenced by surface defects in photovoltaic cells. Recent advancements in defect detection have been largely driven by the widespread use of deep learning models. However, detecting defects at multiple scales especially small ones remains challenging due to the varying sizes of defects on photovoltaic cells. Additionally, the presence of significant noise in the images further complicates the extraction of distinguishable features. To address these challenges, this study proposes a novel, one-stage multi-scale neighbourhood feature interaction network (MNFI-Net) designed to detect defects of various sizes against complex backgrounds. The MNFI-Net architecture includes the following components: (1) Ghost cross-stage module, aimed at reducing redundant information; (2) neighbourhood feature interaction module, which enhances the model's ability to detect defects of different sizes; (3) global attention mechanism that focuses on highlighting key features in the fused feature maps. Additionally, for multi-scale defect detection tasks, we introduced a new balanced efficient loss function. Extensive comparison experiments and ablation studies were conducted on the public photovoltaic electroluminescence image dataset. The experimental results demonstrate that MNFI-Net achieves 94.0% precision and 95.5% mean average precision, outperforming existing state-of-the-art methods in defect classification and detection. The code and proposed models in this study can be accessed at https://github.com/lyc686/MNFI-Net.

Balanced efficient lossDeep learning methodMulti-scale neighbourhood feature interaction networkPhotovoltaic cell defects detection

Liu Y.C.、Hua Q.、Chen L.L.、Dong C.R.、Zhang F.、Zhang Y.

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Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University

Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

2025

10.1016/j.knosys.2024.112882

Knowledge-based systems

Knowledge-based systems

SCI
ISSN：0950-7051
年,卷(期)：2025.309(Jan.30)
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