An Enhanced YOLOv8 Model with Symmetry-Aware Feature Extraction for High-Accuracy Solar Panel Defect Detection

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Abstract
The growing popularity of solar panels is crucial for global decarbonization, but harsh environmental conditions can lead to defects such as cracks, fingerprints, and short circuits. Existing methods face the challenge of detecting multi-scale defects while maintaining real-time performance. This paper proposes a solar panel defect detection model, DCE-YOLO, based on YOLOv8. The model incorporates a C2f-DWR-DRB module for multi-scale feature extraction, where the parallel DRB branch models spatial symmetry through symmetric-rate dilated convolutions, improving robustness and consistency. The COT attention module strengthens long-range dependencies and fuses local and global contexts to achieve symmetric feature representation. The lightweight and efficient detection head improves detection speed and accuracy. The CIoU loss function is replaced with WIoU, and a non-monotonic dynamic focusing mechanism is used to mitigate the effect of low-quality samples. Experimental results show that compared with the YOLOv8 benchmark, DCE-YOLO achieves a 2.1% performance improvement on mAP@50 and a 4.9% performance improvement on mAP@50-95. Compared with recent methods, DCE-YOLO exhibits broader defect coverage, stronger robustness, and a better performance-efficiency balance, making it highly suitable for edge deployment. The synergistic interaction between the C2f-DWR-DRB module and COT attention enhances the detection of symmetric and multi-scale defects under real-world conditions.
Funding Information
  • Shandong Province Science and Technology Small and Medium-sized Enterprises Innovation Ability Improvement Project (2024TSGC0285, 2023GX027)
  • Taian Science and Technology Innovation Development Project (2024TSGC0285, 2023GX027)

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