A photovoltaic defect detection method based on yolov4 and thermal infrared images

Abstract

The invention discloses a photovoltaic defect detection method based on Yolov4 and thermal infrared images, belonging to the technical field of deep learning, neural network and photovoltaic power station defect detection. The specific method is as follows: firstly, the UAV is used to collect the original data, and the collected data is sent back to the background system for image preprocessing; then, an infrared image data set is created, and a priori frame clustering is performed on the marked infrared images; then Put the pictures into the network for training, and output characteristic pictures; finally, the network model is evaluated, and the prediction network is integrated into the photovoltaic detection background system, and the infrared images captured and returned by the drone can be detected in real time. The method of the invention realizes infrared hot spot photovoltaic detection through deep learning, thermal infrared image combined with unmanned aerial vehicle imaging technology, and the operation process is simple. Compared with the traditional photovoltaic hot spot detection method, the method of the invention greatly saves manpower and material resources, and the recognition speed Faster and more accurate hot spot detection.

Description

technical field [0001] The invention relates to the technical field of deep learning, neural network and photovoltaic power station hot spot and crack detection, more specifically, the invention is a photovoltaic defect detection method based on Yolov4 and thermal infrared images. Background technique [0002] As a clean energy, solar energy has always been favored by people. Photovoltaic power generation is currently the most widely used method of solar power generation, and it is also a relatively mature method. It has no noise and radiation hazards, and does not pollute the environment. Independent photovoltaic power generation systems are mostly built in remote areas and will not affect residents. [0003] The photovoltaic defects detected in the present invention include two types of hot spots and cracks. When the photovoltaic module is working, the current of the single cell in the module is reduced due to shading or its own reasons. When the working current exceeds t...

AI Technical Summary

Problems solved by technology

[0005] Traditional target detection methods mainly use manual feature extraction. By screening and checking the captured photos, in practical applications, the infrared images collected by drones have no obvious edge features, and the recognition degree of long-distance and large-scale photovoltaic arrays is relatively low. Low, it is difficult to identify with naked eyes and the workload is heavy

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