Solar photovoltaic panel surface detection

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.

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.

Can solar photovoltaic panel surface defect detection be applied to industrial inspection?

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.

How to detect photovoltaic panel defects?

Since manual detection of photovoltaic panel defects is relatively wasteful of time and cost, the current mainstream detection methods are machine vision and computer vision inspection.

How machine vision is used in photovoltaic panel defect detection?

Machine vision-based approaches have become an important direction in the field of defect detection. Many researchers have proposed different algorithms 11, 15, 16 for photovoltaic panel defect detection by creating their own datasets.

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.