Abnormity monitoring method and system for key components in unmanned aerial vehicle circuit inspection picture
A technology of abnormal monitoring and unmanned aerial vehicles, which is applied to computer components, closed-circuit television systems, color TV components, etc., can solve the problems of poor accuracy of key components and achieve the effect of improving accuracy
Pending Publication Date: 2022-02-25
HUANENG DALI WIND POWER GENERATION CO LTD +2
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Problems solved by technology
During the UAV inspection process, through the photos taken of power line towers and poles, it is possible to identify whether the power equipment parts in the photos are abnormal, so as to judge whether the power line is normal. Less accurate when monitoring exceptions
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[0028] The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
[0029] It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.
[0030] Please see figure 1 , figure 1 It is a schematic flowchart of a method for abnormality monitoring of key components in the UAV circuit inspection photo provided by the embodiment of the present invention.
[0031] In one implementation, the embodiment of the present invention provides a method for abnormality monitoring of key components in the drone circuit inspe...
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The invention provides an abnormity monitoring method and system for key components in unmanned aerial vehicle circuit inspection picture. Firstly, according to a graphic database of the key components, historical inspection data of an unmanned aerial vehicle, historical diagnosis data and the like, training is performed through a deep neural network to obtain an abnormity recognition model of each key component; then, after a visible light image and a current infrared image in current inspection data collected by the unmanned aerial vehicle in the inspection process are subjected to multiple filtering processing through a lifting wavelet algorithm, the visible light image and the current infrared image are subjected to multiple filtering processing through a median filtering algorithm, and a preprocessed visible light image and a preprocessed infrared image after filtering are obtained; and finally, the current image of each key part is segmentrf from the preprocessed visible light image and the preprocessed infrared image, the current image is inputted into the abnormity recognition model, whether the key part is abnormal or not is analyzed, and if the key part is abnormal, an alarm prompt is given. In this way, the accuracy of abnormity recognition of the key components in the circuit is improved.
Description
technical field [0001] The invention relates to the technical field of electric power inspection, in particular, to a method and system for abnormal monitoring of key components in the circuit inspection photos of unmanned aerial vehicles. Background technique [0002] At present, UAV line inspection is playing an increasingly important role in the inspection of transmission lines, which greatly reduces the workload of power service personnel, reduces the probability of possible personnel danger, reduces the maintenance cost of power equipment, and improves the reliability of the power grid. safety and reliability. During the UAV inspection process, through the photos taken of power line towers and poles, it is possible to identify whether the power equipment parts in the photos are abnormal, so as to judge whether the power line is normal. Less accurate when monitoring exceptions. Therefore, it is necessary to provide a solution to improve the accuracy of abnormal identif...
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IPC IPC(8): G06K9/00G06T7/00G06T7/12G06T7/136G06N3/04G06N3/08H04N5/235H04N7/18
CPCG06T7/0004G06T7/12G06T7/136G06N3/04G06N3/08H04N7/181G06T2207/30164G06T2207/20081G06T2207/20084G06T2207/10048H04N23/71H04N23/74
Inventor 张时叶林魏东华陈伟亚庞军周盛龙周泳欣王建国蒋成文李东辉杨鹤松曹云栋杨峰杨超陈仕祥
Owner HUANENG DALI WIND POWER GENERATION CO LTD