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Photovoltaic module fault area image detection method and system

A photovoltaic module and image detection technology, which is applied in the field of target detection, can solve problems such as blurred boundaries of photovoltaic module defects, difficulty in detecting defect areas, and high time complexity of photovoltaic panel defects, so as to improve detection accuracy, simplify model structure, and improve The effect of detection speed

Pending Publication Date: 2021-10-01
车嘉祺
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Problems solved by technology

[0002] Traditional photovoltaic defect detection usually uses template matching method, but the tilt angle of photovoltaic modules in different photovoltaic power plants may change, and the time complexity of using template matching to detect photovoltaic panel defects is high
In addition, the boundaries of photovoltaic module defects are blurred, and it is difficult to detect accurate defect areas only by using template matching methods

Method used

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  • Photovoltaic module fault area image detection method and system
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Embodiment Construction

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0032] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrences of this phrase in various places in the specification are not necessarily all referring to the same embodiment, n...

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Abstract

The invention provides a photovoltaic module fault area image detection method and system, and the method comprises the steps: obtaining a to-be-input image, and carrying out image feature enhancement and data enhancement of the to-be-input image; and performing target detection on the image after feature enhancement and data enhancement based on a preset neural network, and determining a fault area of a photovoltaic module in the image. According to the photovoltaic module fault area image detection method and system provided by the embodiment of the invention, a novel neural network is provided to detect the photovoltaic module image, rich context information in a feature map is fused, and a distraction mechanism is combined, so that the model structure is simplified, the image detection accuracy is improved, and the detection speed is improved.

Description

technical field [0001] The present invention relates to the technical field of target detection, and more particularly, to a method and system for image detection of faulty areas of photovoltaic modules. Background technique [0002] Traditional photovoltaic defect detection usually uses the template matching method, but the tilt angle of photovoltaic modules in different photovoltaic power plants may change, and the time complexity of using template matching to detect photovoltaic panel defects is high. In addition, the boundaries of photovoltaic module defects are fuzzy, and it is difficult to detect accurate defect areas only by template matching. In order to improve the accuracy of photovoltaic module defect detection, the YOLO algorithm in the field of target detection is introduced, and the deep learning method is used to detect the location of photovoltaic module defects. [0003] Therefore, there is an urgent need for an image detection method and system for faulty ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/20032G06T2207/20081G06T2207/20084G06T2207/20016G06N3/045G06F18/253G06T5/90G06T5/70
Inventor 车嘉祺
Owner 车嘉祺
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