A lightweight and efficient model for photovoltaic panel defect

Within this research, we introduce a streamlined yet effective model founded on the “You Only Look Once” algorithm to detect photovoltaic panel defects in intricate settings.

A photovoltaic panel defect detection framework enhanced by deep

Experimental results demonstrate that the proposed model outperforms YOLOv11n and other mainstream lightweight detection algorithms in terms of mAP, precision, and recall, while

LFS-YOLO: A PV Panel Defect Detection Algorithm for Drone Infrared

In this article, a hot spot defect detection algorithm according to infrared images of aerial PV is proposed for practical engineering problems such as defects with different morphology, unclear

A photovoltaic panel defect detection framework enhanced by deep

This paper proposes a photovoltaic panel defect detection method based on an improved YOLOv11 architecture. By introducing the CFA and C2CGA modules, the YOLOv11 model is

ST-YOLO: A defect detection method for photovoltaic modules based

For defect detection in crystalline silicon photovoltaics, the industry currently widely uses technologies such as manual visual inspection, current-voltage (I-V) curve analysis, infrared thermal

Global photovoltaic solar panel dataset from 2019 to 2022

We developed a new method to identify PV panels globally, producing an annual 20-meter resolution dataset for 2019–2022.

Deep-Learning-for-Solar-Panel-Recognition

Recognition of photovoltaic cells in aerial images with Convolutional Neural Networks (CNNs). Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet.

Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based

Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable

Automated detection and tracking of photovoltaic modules from 3D

Real-time detection of PV modules in large-scale plants under varying lighting conditions. Automatic monitoring and evaluation of individual PV module performance. Development of

YOLO-LitePV: a lightweight detection algorithm for photovoltaic panel

To address the low operational efficiency of detection algorithms and the low accuracy due to the similarity and large-scale variance of PV defects, we propose an improved lightweight