Hybrid CNN Architecture for Hot Spot Detection in Photovoltaic Panels
Instead of processing high-resolution images entirely with a single deep neural network, this cascading approach separates the tasks into distinct stages: initial detection of photovoltaic (PV)
ISPRS-Annals
To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras.
(PDF) Detecting Solar Panel Hotspots and Diode Failures with
This research paper explores the use of deep learning, specifically the YOLOv11 model, in detecting defects in solar panels using thermal imaging. The focus is on two common types of
A novel detection method for hot spots of photovoltaic (PV) panels
This model is a detection method for hot spots of PV panels based on the latest generation of the one-stage object detection YOLOv5 network, which is improved to achieve rapid
Deep Learning-Based Hot Spot Detection in Aerial Infrared
To address these challenges, we propose a rapid detection method for hot spots in photovoltaic panels using deep convolutional neural networks, combined with unmanned aerial
IoT System Based on Artificial Intelligence for Hot Spot Detection in
For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity
Accurate detection of bright spots in electro-luminescence images of
As a baseline, we first applied object detection models directly on PV panel images to identify bright spots. We utilized annotations provided by the subject matter expert (SME) to train the
Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in
The existing hot-spot fault detection methods of photovoltaic panels cannot adequately complete the real-time detection task; hence, a detection model considering both detection accuracy
Applying RetinaNet Machine Learning Models for Hot‐Spot Detection
In this study, our research group proposes an application of RetinaNet to develop a model capable of detecting hot spots in photovoltaic panels through processing thermal images. © 2025
Accurate Detection of Bright Spots in Solar Photovoltaic Panels
In this paper, we propose a robust machine learning (ML) based approach to accurately detect bright spots by optimally splitting the EL images of PV solar panels and engineering novel discriminative
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