Abstract The continuous increase in the number and scale of solar photovoltaic power plants requires the implementation of reliable diagnostic tools for fault detection. With the recent
The future of active infrared imaging for defect detection in the renewable and electronic industries will be characterized by advancements in
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To address these issues, this paper proposes a photovoltaic panel near-infrared defect detection method based on the FBCT-YOLOv8 algorithm.Built upon YOLOv8, the proposed method
Abstract Defect detection in photovoltaic (PV) modules and their impact assessment is important to enhance the PV system performance and reliability. To identify and analyze the defects,
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 states, and then
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Rodriguez-Vazquez et al. implemented CenterNet-based key point detection to detect solar panels in UAV images. The goal was to use field-level automated analysis to quickly and
Passive thermography – a reliable and established method for functional testing and characterization of large solar modules – quickly reaches its limits when it comes
Abstract: The photovoltaic (PV) industry is essential to global renewable energy generation; however, inefficient and accurate fault detection in PV panels remains challenging.
Localisation, detection and identification of defective PV modules, along with determination of the nature of the defects, is essential for ensuring sustainability and maximization of
In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common
The main objective of the study is to develop a Convolutional Neural Network (CNN) model to detect and classify failures in solar panels. By utilizing a large-scale IR image dataset
Timely automated detection is crucial for maintaining power generation efficiency and ensuring equipment safety. This paper presents a lightweight enhanced YOLOv11n model for
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In this paper, the equipment used for collecting the infrared thermal images of PV panels was an infrared camera (FLUKE Ti 450), which is often used to acquire the thermal images of PV arrays in operation,
The increasing reliance on photovoltaic systems (PVS) for sustainable energy production necessitates efficient monitoring methods to ensure optimal performance and timely fault detection.
A dynamically adaptive and high-efficiency small object detection network for infrared thermographic images in online monitoring of solar photovoltaic panel defects
With the continuously increasing application of photovoltaic (PV) panels, how to effectively manage these valuable facilities has become an issue of concern. To date, some methods have been
Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to
Abstract This paper presents a comparative study on the application of drone-assisted infrared thermography coupled with state-of-the-art machine learning models, including Vision
Recently, fault localisation, detection and diagnosis of photovoltaic (PV) plants using infrared (IR) thermographic imaging combined with advanced deep learning (DL) methods have
Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine
real-time detection of PV panels failures. The YOLOv5 model was trained by sets sorted into 9different categories including fault and abnormal objects'' coverage. This multi-class
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