Photovoltaic panel surface detection

What is PV panel surface-defect detection technology?

Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections.

How to detect surface dust on solar photovoltaic panels?

At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image segmentation and instance segmentation, super-resolution image generation, multispectral and thermal infrared imaging, and deep learning methods.

What is photovoltaic surface defect detection?

Then, the network weights are used to identify and detect actual photovoltaic defects, thus providing a new concept for photovoltaic surface defect detection. For example, a convolutional neural network (CNN) can be used to extract defect features and help the network improve its ability to express defect feature information.

What is PV panel defect detection?

The task of PV panel defect detection is to identify the category and location of defects in EL images.

Can computer vision detect PV panel surface defects?

Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to {accurately identify and detect such defects}.

How to detect solar photovoltaic panels?

Among them, algorithms such as YOLO [11, 12], Faster R-CNN , and RetinaNet [14, 15] in object detection methods can accurately mark the position and boundary of solar photovoltaic panels in the image, but due to the need for a large amount of computing resources, they have high requirements for hardware and environment.