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An efficient solar panel crack detection system using a novel solcnn model and crack segmentation algorithm
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DOI：10.1016/j.rineng.2026.108995.png)
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Abstract
En 中文
• SOLCNN classifies cracked/non-cracked solar panels with 98.95% average detection accuracy. • Cracked panel detection rate: 99.3%; non-cracked panel detection rate: 98.6%. • Image enhancement boosts Crack Sensitivity, Specificity, and Accuracy to over 98%. • Image enhancement improved crack detection accuracy from 96.07% to 98.7%.
Keywords:
Solar
CNN
segmentation
Renewable
Sustainable
Clean Energy
ANN
Artificial Neural Network
CCD
Charge Coupled Device
CNN
Convolutional Neural Network
CRSA
Crack Segmentation Algorithm
DL
Deep Learning
EL
Electroluminescence
FIS
Fuzzy Inference System
MAU-Net
Multi-Attention U-Net
ML
Machine Learning
MPPT
Maximum Power Point Tracking
NB-CNN
Neighbor-Based Convolutional Neural Network
ORing
Orientation-based Ringing Technique
PL
Photoluminescence
PV
Photovoltaic
SMOTE
Synthetic Minority Oversampling Technique
SOLCNN
Solar Convolutional Neural Network
SP
Solar Panel
SVM
Support Vector Machine
VL
Visible Light Imaging
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