Through this efficient data collection method, we gathered a large number of high-definition images of photovoltaic panels. These captured images were processed to form the infrared defect image
One of the significant challenges is the fault identification of the solar PV module, since a vast power plant condition monitoring of individual panels is cumbersome. This paper attempts to
We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including
To address these limitations (Hussain & Khanam, 2024), this study proposes a PV panel defect detection method based on YOLOv8 and computer-based infrared vision. We modify the
One of the most effective ways to monitor solar panels for early signs of problems is by using thermal imaging. Infrared (IR) anomaly detection has become a powerful tool for spotting
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
In this work, we propose Deep Res-UNet for segmentation of UAV-based infrared images for photovoltaic panels. Infrared images are collected by the UAV equipped with infrared thermal
In one fell swoop, the IR cameras on drones made it so easy to record large quantities of images of many PV modules in large PV power stations that it was no longer possible or practical to
This paper attempts to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique.
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