Abnormal target detection method, system and device for photovoltaic panel and medium

Abstract

The invention discloses an abnormal target detection method, system and device for a photovoltaic panel and a medium, and the method comprises the steps: obtaining a photovoltaic panel image in a photovoltaic power station, inputting the photovoltaic panel image into a segmentation model, so as to obtain the segmentation data of the photovoltaic panel, the photovoltaic panel segmentation data comprises background-removed photovoltaic panel image information and first position information corresponding to each photovoltaic panel, identifying an abnormal target in the photovoltaic panel segmentation data by using a target detection model, and outputting second position information of the abnormal target, the abnormal photovoltaic panel where the abnormal target is located is determined based on the first position information and the second position information, so that the abnormal target can be rapidly and accurately identified after background interference is removed, loss caused by accumulation of foreign matters on the photovoltaic panel is avoided, the power generation amount of a photovoltaic electric field is improved, the power generation safety of a photovoltaic power station can be enhanced, and the power generation efficiency of the photovoltaic power station is improved. And meanwhile, waste of human resources caused by manual inspection can be avoided.

Description

technical field [0001] The invention relates to the technical field of detection, in particular to a method, system, equipment and medium for detecting abnormal targets of photovoltaic panels. Background technique [0002] With the development of my country's national economy, vigorous investment and construction of new energy industries, especially photovoltaic power generation as a new, clean and renewable energy in the 21st century. In photovoltaic stations that have been constructed and put into operation, photovoltaic panels are placed outdoors, and foreign objects such as dust, bird droppings, and coverings are easy to accumulate on the surface, which directly affects the power generation efficiency and service life of photovoltaic panels. [0003] For the detection of abnormal targets on the surface of photovoltaic panels, there are currently three main methods: artificial subjective judgment, pre-installed sensors, and computer vision-based detection methods. The de...

AI Technical Summary

Problems solved by technology

The defects of the traditional artificial subjective judgment method include low detection efficiency, high labor cost, high risk, and strong subjectivity

The defects of the dust sensor include high installation cost, low sensitivity to some types of foreign objects, high operation and maintenance costs, etc.

Computer vision-based detection is currently mainly based on traditional image processing methods based on threshold binarization, or using a single neural network to detect dust / foreign objects in photovoltaic panels in aerial images of drones, but it is difficult to accurately detect foreign objects based on computer vision methods. On the one hand, the background of the pictures taken by the UAV is very complicated. On the other hand, the size of foreign objects is uncertain. Most of them are small in size and occupy a small proportion in the photovoltaic panel. Therefore, the accuracy of using a single end-to-end detection model is very low.

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