DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

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1. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images D. Manno et al.
2. An instance segmentation based global MPPT method for PV systems under partial shading condition E. Rahman et al.
3. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning S. Ren et al.
4. Deep learning-based model for fault classification in solar modules using infrared images P. Haidari et al.
5. Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features N. Sridharan et al.
6. Fault classification using deep learning based model and impact of dust accumulation on solar photovoltaic modules R. Aman et al.
7. Performance of fault classification on Photovoltaic modules using Thermographic images A. Raorane et al.
8. A Comparison and Introduction of Novel Solar Panel’s Fault Diagnosis Technique Using Deep-Features Shallow-Classifier through Infrared Thermography W. Ahmed et al.
9. Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme R. Eltuhamy et al.
10. Güneş Paneli Kusurlarının Derin Öğrenme Tabanlı Sınıflandırılması S. Lermi & T. Onur
11. Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies F. Demir
12. Deep learning model for detection of hotspots using infrared thermographic images of electrical installations E. Ukiwe et al.
13. SolarFaultNet: CNN-based photovoltaic fault classification via channel-wise attention mechanisms M. Alanazi
14. A Relational Conceptual Model in GIS for the Management of Photovoltaic Systems F. Piccinini et al.
15. Deep neural network based approach for detection of defective solar cell A. Acharya et al.
16. Deep Learning Based Fault Detection and Diagnosis in Photovoltaic System Using Thermal Images Acquired by UAV B. KAYCI et al.
17. Solar cell surface defect inspection based on multispectral convolutional neural network H. Chen et al.
18. Evaluation of The Studies on Unmanned Aircraft System Safety Management Systems with Bibliometric Analysis O. Altıntaş et al.
19. Application of Artificial Intelligence for Selected Photovoltaic Faults Detection Based on Electroluminescence Images L. Jasińska et al.
20. LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification S. Lee et al.
21. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review E. Boateng et al.
22. Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances A. Kandeal et al.
23. Deep learning-based automated defect classification in Electroluminescence images of solar panels H. Munawer Al-Otum
24. Integrative deep learning architectures and convolutional neural networks for advanced fault classification in photovoltaic modules R. Raj et al.
25. Photovoltaic defect classification through thermal infrared imaging using a machine learning approach C. Dunderdale et al.
26. A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis A. Michail et al.
27. Advanced deep learning modeling to enhance detection of defective photovoltaic cells in electroluminescence images M. Ebied et al.
28. Energy-agriculture nexus: Exploring the future of artificial intelligence applications M. Kabir & S. Ekici
29. SolarFaultAttentionNet: Dual-Attention Framework for Enhanced Photovoltaic Fault Classification M. Alanazi & Y. Alamri
30. Development of a CNN-based hierarchical inspection system for detecting defects on electroluminescence images of single-crystal silicon photovoltaic modules K. Lin et al.
31. Thermal image-driven CNN for predicting solar photovoltaic module lifespan from hotspots A. Raorane et al.
32. Instant testing and non-contact diagnosis for photovoltaic cells using K-means clustering and associated hyperspectral imaging E. Attia et al.
33. Half and full solar cell efficiency binning by deep learning on electroluminescence images Y. Buratti et al.
34. Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data W. Luan et al.
35. Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review A. de Oliveira et al.
36. Edge-Computing-Enabled Unmanned Module Defect Detection and Diagnosis System for Large-Scale Photovoltaic Plants X. Li et al.
37. An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array S. Tchoketch Kebir et al.
38. Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles I. Segovia Ramírez et al.
39. Enhancing Solar Plant Efficiency: A Review of Vision-Based Monitoring and Fault Detection Techniques I. Polymeropoulos et al.
40. Anomaly detection in IR images of PV modules using supervised contrastive learning L. Bommes et al.
41. Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects M. Mansouri et al.
42. Low-cost AI-based solar panel detection drone design and implementation for solar power systems T. Özer & Ö. Türkmen
43. Design and Implementation of Vision Based Autonomous Rover for Solar Panel Inspection S. Jagadeeswaran et al.
44. Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks R. Colmenares-Quintero et al.
45. PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility M. Hussain et al.
46. An efficient and portable solar cell defect detection system M. El-Rashidy
47. Cost-Effective Flight Strategy for Aerial Thermography Inspection of Photovoltaic Power Plants V. Lofstad-Lie et al.
48. PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification B. Wang et al.
49. Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring M. Ali et al.
50. Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image Q. Zheng et al.
51. Fault Detection in Solar Energy Systems: A Deep Learning Approach Z. Duranay
52. Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency F. Almalki et al.
53. A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision T. Hussain et al.
54. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti
55. In-Depth Review of YOLOv1 to YOLOv10 Variants for Enhanced Photovoltaic Defect Detection M. Hussain & R. Khanam
56. Artificial Vision in Renewable Photovoltaic Systems: A Review and Vision of Specific Applications and Technologies T. Amaral et al.
57. A taxonomical review on recent artificial intelligence applications to PV integration into power grids C. Feng et al.
58. Fault Detection in aerial images of photovoltaic modules based on Deep learning S. Naveen Venkatesh & V. Sugumaran
59. Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features N. Sridharan & V. Sugumaran
60. An Unmanned Inspection System for Multiple Defects Detection in Photovoltaic Plants X. Li et al.
61. Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network J. Balzategui et al.
62. Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms H. Hwang et al.
63. Applied imagery pattern recognition for photovoltaic modules’ inspection: A review on methods, challenges and future development Z. Yahya et al.
64. IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances L. Cardinale-Villalobos et al.
65. A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces M. Hussain et al.
66. 75MW AC PV Module Field Anomaly Detection Using Drone-Based IR Orthogonal Images With Res-CNN3 Detector K. Masita et al.
67. A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules S. Venkatesh & V. Sugumaran
68. Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification D. Rocha et al.
69. Open data sets for assessing photovoltaic system reliability X. Chen et al.
70. Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules K. Awedat et al.
71. Logic Expression and Structure Simplification for SFEP of Sensor System T. Cui & S. Li
72. Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images R. Pierdicca et al.
73. Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis S. Ren et al.
74. Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery M. Vlaminck et al.
75. Automatic fault detection of utility-scale photovoltaic solar generators applying aerial infrared thermography and orthomosaicking A. Oliveira et al.
76. Automated Detection of Shading Faults in Photovoltaic Modules Using Convolutional Neural Networks and I–V Curves J. Arenas-Prado et al.
77. Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms R. Yu et al.
78. ESD-YOLOv8: An Efficient Solar Cell Fault Detection Model Based on YOLOv8 L. Zhang et al.
79. Deep learning-based ensemble model for classification of photovoltaic module visual faults N. Sridharan & V. Sugumaran
80. Fault diagnosis of photovoltaic modules: A review Y. Liu & Y. Wu
81. Fault‐Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models I. Khosa et al.
82. Automatic Detection of Visual Faults on Photovoltaic Module Using Deep Ensemble Learning Network S. Venkatesh et al.
83. Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line I. Hussain et al.
84. Computer vision tool for detection, mapping, and fault classification of photovoltaics modules in aerial IR videos L. Bommes et al.
85. Fault diagnosis of visual faults in photovoltaic modules: A Review N. S & V. Sugumaran
86. Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset M. Tomaszewski et al.
87. Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module N. Sridharan & V. Sugumaran
88. A deep learning-based method detects dust from solar PV panels through Unmanned Aerial Vehicles Y. Gao & S. Li
89. Navigating the empirical digital twin space of solar cells: Semantic compression incorporating j0 and Rs images for systematic defect analysis P. Kunze et al.