To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of photovoltaic panel defect detection, this article proposes an infrared detection method based on computer vision, with enhancements built upon the YOLOv8 model.
The introduction of infrared image technology provides a new idea for the defect detection of solar panels. By capturing the temperature distribution and thermal anomalies on the surface of solar panels, infrared imaging technology can detect defects more accurately, providing a more sensitive means for timely detection.
In an era of rapid advancements in artificial intelligence and the booming growth of the renewable energy industry, detecting defects in PV panels accurately and effectively using infrared imaging based on the principle of electroluminescence holds immense practical value.
Traditional solar panel defect detection methods mainly rely on computer vision and image processing technology, which are realized by manual design rules and feature extraction methods.
To address this issue, a new PV panel condition monitoring and fault diagnosis technique is developed in this paper. The new technique uses a U-Net neural network and a classifier in
To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of
Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues,
In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep
Keywords—photovoltaic system, solar energy, solar panels, infrared imaging, image processing, computer vision, machine learning, object detection, infrared thermography I.
Keywords: radiometric infrared thermography; solar photovoltaic panel; intelligent inspection; predictive maintenance; intelligent fault detection and diagnosis; explainable artificial
monitoring and fault diagnosis based on mask images can be guaranteed to a large extent. In the research, 295 infrared images were taken first from the PV panels in different health
There are potential safety hazards associated with defects in solar panels, and traditional detection methods suffer from low efficiency and limited application ranges. Currently, machine vision
This paper based on U-Net network and HSV space, proposes a method of PV infrared image segmentation and location detection of hot spots, which is used to detect and analyze the
In future developments, the model could be extended to identify visible panel defects from standard RGB images, making it a versatile solution for real-time PV system monitoring. Keywords:
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