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Wood panel products are essential materials used in construction for applications such as flooring, siding, and roofing, typically available in standard dimensions of 4 feet by 8 feet, with thicknesses varying from one-quarter of an inch to one and one-eighth inches. Among the most common types of wood panels is plywood, which is produced by gluing multiple layers of thin wood veneers under pressure. The grain of the outer veneers runs lengthwise, while the grains of the interior layers run...
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Foreign object detection on photovoltaic panels based on DHLNet.

Haibo Jin1, Mengjiao Li2, Xiaoyun Lv2

  • 1Faculty of Software, Liaoning Technical University, Huludao, 125105, Liaoning, China. jinhaibo@lntu.edu.cn.

Scientific Reports
|February 9, 2026
PubMed
Summary

This study introduces DHLNet, an advanced model for detecting foreign objects on photovoltaic panels. DHLNet enhances detection accuracy and supports real-time monitoring, crucial for PV power plant safety and efficiency.

Keywords:
Cross knowledge hybrid blockDual-flow feature pyramid networkForeign object detectionLarge kernel self-attentionPV panel

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Area of Science:

  • Renewable Energy Systems
  • Computer Vision
  • Artificial Intelligence

Background:

  • Foreign object accumulation on photovoltaic (PV) panels degrades performance and can cause damage.
  • Challenges in detection include low contrast, complex object shapes, and imbalanced datasets.
  • Automated detection is vital for the safe and economic operation of PV power plants.

Purpose of the Study:

  • To propose an effective and efficient model for automated foreign object detection on PV panels.
  • To address limitations of existing methods in detecting small objects in complex backgrounds.
  • To improve the accuracy and reliability of foreign object identification for PV systems.

Main Methods:

  • Development of the DHLNet model, featuring a Dual-Flow Feature Pyramid Network for improved small object detection.
  • Integration of a high-frequency enhancement module to improve sensitivity to blurred edges.
  • Incorporation of a large-separable-kernel attention mechanism for enhanced feature extraction.

Main Results:

  • DHLNet achieved mAP@0.5 of 79.9% and mAP@0.5:0.95 of 58.8%, outperforming the original YOLO11n algorithm.
  • The F1 score reached 0.761, a 1.3% improvement.
  • The model demonstrated real-time inference capabilities, suitable for on-site and online monitoring.

Conclusions:

  • DHLNet effectively detects foreign objects on PV panels, improving upon existing algorithms.
  • The model's lightweight structure and enhanced features offer practical potential for automated PV inspection.
  • DHLNet contributes to the safe and efficient operation of photovoltaic power plants through reliable foreign object detection.