When solar photovoltaic panel surface defect detection is applied to industrial inspection, the primary focus lies in achieving a highly accurate and precise model with exceptional localization capabilities, and the training model will basically not affect the detection speed.
In order to avoid such accidents, it is a top priority to carry out relevant quality inspection before the solar panels leave the factory. For the defect detection of solar panels, the main traditional methods are divided into artificial physical method and machine vision method.
Policies and ethics Nowadays, the photovoltaic industry has developed significantly. Solar photovoltaic panel defect detection is an important part of solar photovoltaic panel quality inspection. Aiming at the problems of chaotic distribution of defect targets on photovoltaic panels,...
Further examples, such as You Only Look Once-v3 (YOLO-v3) , MobileNetV2 , and ShuffleNet , demonstrate the potential for photovoltaic (PV) surface defect detection.
To overcome the limitation of detection accuracy and speed, an improved photovoltaic surface defect detection method is proposed in this paper. You Only Look Once-v5 (YOLO-v5) is adopted as the main method.
The methodology involved in the fault classification and early detection of solar panel faults begins with the selection of the dataset. Two types of image datasets are used in this case, namely the aerial image dataset of solar panels and the electroluminescence image dataset of solar panel cells.