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This research aims to enhance the detection accuracy of faults in photovoltaic panels using an advanced algorithm based on thermal imaging.

April 17, 2026Sensors and MaterialsOpen Access

Enhanced Photovoltaic–You Only Look Once Model for Photovoltaic Panel Fault Detection Based on IR Thermal Imaging Sensors

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Novel model improves fault detection accuracy in photovoltaic panels using thermal imaging sensors, suggesting efficient solutions.

This research aims to enhance the detection accuracy of faults in photovoltaic panels using an advanced algorithm based on thermal imaging.
Developed a photovoltaic fault detection model called PV-YOLO using IR thermal imaging sensors.
Implemented dynamic convolution kernels through weighted fusion of expert convolution kernels.
Utilized a multibranch dilated convolution structure for a larger receptive field.
Incorporated a multi-attention fusion mechanism for improved detection accuracy.
Achieved 85.3% Mean Average Precision at Intersection over Union 0.5, surpassing the YOLOv8n model by 5.2 percentage points.
Increased recall rate to 80.5%, an improvement of 4.2 percentage points compared to prior models.
Demonstrated better balance between detection accuracy and model complexity than other mainstream algorithms.

Jia et al. (2026) studied this question.

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