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Photovoltaic Cell Surface Defect Detection via Subtle Defect Enhancement and Background Suppression.

Yange Sun1, Guangxu Huang1, Chenglong Xu1

  • 1School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.

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|September 27, 2025
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Summary
This summary is machine-generated.

This study introduces a novel PV Cell Surface Defect Detector (PSDD) to accurately identify subtle defects in photovoltaic (PV) cells. The new method significantly improves defect detection accuracy, enhancing solar energy conversion efficiency.

Keywords:
attention mechanismdeep learning methodsphotovoltaic cell defects detectionsubtle defect detection

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Vision

Background:

  • Photovoltaic (PV) cells are crucial for solar energy, but manufacturing defects like cracks and interruptions reduce efficiency.
  • Existing defect detection methods struggle with subtle surface flaws and background noise.
  • Accurate defect identification is vital for optimizing PV cell performance and reliability.

Purpose of the Study:

  • To develop an advanced PV Cell Surface Defect Detector (PSDD) for precise identification and localization of subtle defects.
  • To enhance the feature representation of fine defects and suppress background noise during detection.
  • To improve the overall accuracy and robustness of defect detection in PV cells.

Main Methods:

  • Proposed a PV Cell Surface Defect Detector (PSDD) incorporating a Subtle Feature Refinement Module (SFRM) and a Background Noise Suppression Block (BNSB).
  • SFRM refines fine-grained features by rearranging spatial information and using attention mechanisms to highlight defect-related channels.
  • BNSB employs a dual-path strategy with a Background-Aware Module (BAM) and residual structure for multi-scale feature fusion and noise reduction.

Main Results:

  • The proposed PSDD achieved superior performance in detecting subtle surface defects on PV cells.
  • PSDD demonstrated the highest mAP50 score of 93.6% on the PVEL-AD dataset, outperforming existing methods.
  • The integrated SFRM and BNSB modules effectively enhanced feature representation and suppressed background noise.

Conclusions:

  • The novel PSDD, with its SFRM and BNSB components, offers a significant advancement in PV cell surface defect detection.
  • This method effectively addresses the challenges posed by subtle defects and complex background noise.
  • The improved detection accuracy holds potential for enhancing the quality control and efficiency of photovoltaic manufacturing.