A Survey of CNN-Based Approaches for Crack Detection in Solar PV
Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and long-term reliability. The development of convolutional neural networks (CNNs) has significantly
A novel internal crack detection method for photovoltaic (PV) panels
This paper provides a crack detection method for PV panels based on the Lamb wave, which mainly includes the development of an experimental inspection device and the construction of
Micro-Fracture Detection in Photovoltaic Cells with Hardware
This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alert of a potential failure of the photovoltaic system by using computer vision techniques.
Halcon-Based Solar Panel Crack Detection
In this paper, a solar panel crack detection device based on the deep learning algorithm in Halcon image processing software is designed for the most common defect in solar panel production process,
A photovoltaic panel defect detection framework enhanced by deep
This paper presents a lightweight object detection algorithm based on an improved YOLOv11n, specifically designed for photovoltaic panel defect detection. The goal is to enhance the
Electroluminescence Imaging for Microcrack Detection in Solar Cells
Solar photovoltaic power generation component fault detection system that enables real-time monitoring of cracks and hot spots in solar panels through automated, remote detection.
Deep Learning Approaches for Crack Detection in Solar PV Panels
The results of the crack detection analysis in solar PV panels are presented below, including quantitative values, mathematical formulas, and detailed analysis.
ResNet-based image processing approach for precise detection of
Advancing renewable energy solutions requires efficient and durable solar Photovoltaic (PV) modules. A novel mechanism based on Deep Learning (DL) and Residual Network (ResNet) for accurate
YOLOv7-tiny-based lightweight and efficient algorithm for photovoltaic
This paper presents a novel detection model based on an enhanced version of YOLOv7-tiny to address the challenges in photovoltaic cell defect detection.