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. 10.1016/j.enconman.2021.114315
2. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning S. Ren et al. 10.3390/ijgi11040222
3. Deep learning-based model for fault classification in solar modules using infrared images P. Haidari et al. 10.1016/j.seta.2022.102110
4. Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features N. Sridharan et al. 10.1016/j.egyr.2024.03.044
5. Fault classification using deep learning based model and impact of dust accumulation on solar photovoltaic modules R. Aman et al. 10.1080/15567036.2023.2205859
6. Performance of fault classification on Photovoltaic modules using Thermographic images A. Raorane et al. 10.1051/itmconf/20224403065
7. Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review B. Li et al. 10.1016/j.rser.2020.110512
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. 10.3390/en16031043
9. Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme R. Eltuhamy et al. 10.3390/s23031280
10. Güneş Paneli Kusurlarının Derin Öğrenme Tabanlı Sınıflandırılması S. Lermi & T. Onur 10.7240/jeps.1383975
11. Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies F. Demir 10.1109/ACCESS.2025.3551749
12. Deep learning model for detection of hotspots using infrared thermographic images of electrical installations E. Ukiwe et al. 10.1186/s43067-024-00148-y
13. A Relational Conceptual Model in GIS for the Management of Photovoltaic Systems F. Piccinini et al. 10.3390/en13112860
14. Deep neural network based approach for detection of defective solar cell A. Acharya et al. 10.1016/j.matpr.2020.09.048
15. Deep Learning Based Fault Detection and Diagnosis in Photovoltaic System Using Thermal Images Acquired by UAV B. KAYCI et al. 10.2339/politeknik.1094586
16. Solar cell surface defect inspection based on multispectral convolutional neural network H. Chen et al. 10.1007/s10845-018-1458-z
17. Evaluation of The Studies on Unmanned Aircraft System Safety Management Systems with Bibliometric Analysis O. Altıntaş et al. 10.51785/jar.1453011
18. Application of Artificial Intelligence for Selected Photovoltaic Faults Detection Based on Electroluminescence Images L. Jasińska et al. 10.1109/ACCESS.2025.3589674
19. PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network L. Lüer et al. 10.1016/j.solmat.2023.112621
20. LIRNet: A Lightweight Inception Residual Convolutional Network for Solar Panel Defect Classification S. Lee et al. 10.3390/en16052112
21. Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances A. Kandeal et al. 10.1016/j.solener.2021.05.032
22. Deep learning-based automated defect classification in Electroluminescence images of solar panels H. Munawer Al-Otum 10.1016/j.aei.2023.102147
23. Photovoltaic defect classification through thermal infrared imaging using a machine learning approach C. Dunderdale et al. 10.1002/pip.3191
24. A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis A. Michail et al. 10.1016/j.heliyon.2024.e23983
25. Energy-agriculture nexus: Exploring the future of artificial intelligence applications M. Kabir & S. Ekici 10.1016/j.nexus.2023.100263
26. Development of a CNN-based hierarchical inspection system for detecting defects on electroluminescence images of single-crystal silicon photovoltaic modules K. Lin et al. 10.1016/j.mtcomm.2022.103796
27. In-depth investigation of applied digital photogrammetry to imagery-based RGB and thermal infrared aerial inspection of large-scale photovoltaic installations Y. Zefri et al. 10.1016/j.rsase.2021.100576
28. Instant testing and non-contact diagnosis for photovoltaic cells using K-means clustering and associated hyperspectral imaging E. Attia et al. 10.1007/s42452-023-05431-7
29. Half and full solar cell efficiency binning by deep learning on electroluminescence images Y. Buratti et al. 10.1002/pip.3484
30. Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data W. Luan et al. 10.3390/su152316343
31. Machine vision based fault diagnosis of photovoltaic modules using lazy learning approach S. Naveen Venkatesh & V. Sugumaran 10.1016/j.measurement.2022.110786
32. Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review A. de Oliveira et al. 10.3390/en15062055
33. Edge-Computing-Enabled Unmanned Module Defect Detection and Diagnosis System for Large-Scale Photovoltaic Plants X. Li et al. 10.1109/JIOT.2020.2983723
34. An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array S. Tchoketch Kebir et al. 10.3390/su13116194
35. Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles I. Segovia Ramírez et al. 10.1002/pip.3479
36. Enhancing Solar Plant Efficiency: A Review of Vision-Based Monitoring and Fault Detection Techniques I. Polymeropoulos et al. 10.3390/technologies12100175
37. Anomaly detection in IR images of PV modules using supervised contrastive learning L. Bommes et al. 10.1002/pip.3518
38. Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects M. Mansouri et al. 10.1109/ACCESS.2021.3110947
39. Low-cost AI-based solar panel detection drone design and implementation for solar power systems T. Özer & Ö. Türkmen 10.1108/RIA-03-2023-0022
40. Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks R. Colmenares-Quintero et al. 10.1080/23311916.2021.1981520
41. PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility M. Hussain et al. 10.3390/en15228667
42. An efficient and portable solar cell defect detection system M. El-Rashidy 10.1007/s00521-022-07464-2
43. Cost-Effective Flight Strategy for Aerial Thermography Inspection of Photovoltaic Power Plants V. Lofstad-Lie et al. 10.1109/JPHOTOV.2022.3202072
44. PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification B. Wang et al. 10.1016/j.apenergy.2024.124187
45. Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring M. Ali et al. 10.3390/fi16060191
46. Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image Q. Zheng et al. 10.3390/s22124617
47. Fault Detection in Solar Energy Systems: A Deep Learning Approach Z. Duranay 10.3390/electronics12214397
48. Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency F. Almalki et al. 10.1155/2022/7741535
49. A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision T. Hussain et al. 10.3390/en16104012
50. GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data R. Pierdicca & M. Paolanti 10.5194/gi-11-195-2022
51. In-Depth Review of YOLOv1 to YOLOv10 Variants for Enhanced Photovoltaic Defect Detection M. Hussain & R. Khanam 10.3390/solar4030016
52. A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle A. Di Tommaso et al. 10.1016/j.renene.2022.04.046
53. A taxonomical review on recent artificial intelligence applications to PV integration into power grids C. Feng et al. 10.1016/j.ijepes.2021.107176
54. Fault Detection in aerial images of photovoltaic modules based on Deep learning S. Naveen Venkatesh & V. Sugumaran 10.1088/1757-899X/1012/1/012030
55. Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features N. Sridharan & V. Sugumaran 10.1080/15567036.2021.2020379
56. An Unmanned Inspection System for Multiple Defects Detection in Photovoltaic Plants X. Li et al. 10.1109/JPHOTOV.2019.2955183
57. Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network J. Balzategui et al. 10.3390/s21134361
58. Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms H. Hwang et al. 10.1109/ACCESS.2021.3063461
59. Applied imagery pattern recognition for photovoltaic modules’ inspection: A review on methods, challenges and future development Z. Yahya et al. 10.1016/j.seta.2022.102071
60. IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances L. Cardinale-Villalobos et al. 10.3390/s23156749
61. Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions A. Mellit & S. Kalogirou 10.1016/j.rser.2021.110889
62. A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces M. Hussain et al. 10.1109/ACCESS.2022.3178588
63. 75MW AC PV Module Field Anomaly Detection Using Drone-Based IR Orthogonal Images With Res-CNN3 Detector K. Masita et al. 10.1109/ACCESS.2022.3194547
64. A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules S. Venkatesh & V. Sugumaran 10.1177/1748006X211020305
65. Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification D. Rocha et al. 10.1109/JPHOTOV.2023.3236188
66. Open data sets for assessing photovoltaic system reliability X. Chen et al. 10.1016/j.apenergy.2025.126132
67. Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules K. Awedat et al. 10.1038/s41598-025-06646-x
68. Logic Expression and Structure Simplification for SFEP of Sensor System T. Cui & S. Li 10.1109/JSEN.2020.3044709
69. Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images R. Pierdicca et al. 10.3390/en13246496
70. Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis S. Ren et al. 10.1016/j.apenergy.2022.119876
71. Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery M. Vlaminck et al. 10.3390/s22031244
72. Automatic fault detection of utility-scale photovoltaic solar generators applying aerial infrared thermography and orthomosaicking A. Oliveira et al. 10.1016/j.solener.2023.01.058
73. Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms R. Yu et al. 10.1088/1742-6596/2678/1/012011
74. ESD-YOLOv8: An Efficient Solar Cell Fault Detection Model Based on YOLOv8 L. Zhang et al. 10.1109/ACCESS.2024.3466209
75. Deep learning-based ensemble model for classification of photovoltaic module visual faults N. Sridharan & V. Sugumaran 10.1080/15567036.2022.2083729
76. Fault diagnosis of photovoltaic modules: A review Y. Liu & Y. Wu 10.1016/j.solener.2025.113489
77. Fault‐Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models I. Khosa et al. 10.1155/2023/2663150
78. Automatic Detection of Visual Faults on Photovoltaic Module Using Deep Ensemble Learning Network S. Venkatesh et al. 10.2139/ssrn.4144366
79. Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line I. Hussain et al. 10.3390/en15145106
80. Computer vision tool for detection, mapping, and fault classification of photovoltaics modules in aerial IR videos L. Bommes et al. 10.1002/pip.3448
81. Fault diagnosis of visual faults in photovoltaic modules: A Review N. S & V. Sugumaran 10.1080/15435075.2020.1825443
82. Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset M. Tomaszewski et al. 10.3390/app10062104
83. Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module N. Sridharan & V. Sugumaran 10.1080/15567036.2021.1905753
84. A deep learning-based method detects dust from solar PV panels through Unmanned Aerial Vehicles Y. Gao & S. Li 10.1088/1742-6596/2584/1/012019