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Airfield runway foreign matter detection and classification method based on unmanned aerial vehicle high-low altitude combined scanning

A technology for airport runway and foreign object detection, applied in the field of image recognition, can solve the problems of too small foreign objects, low efficiency of UAV low-altitude inspection, and difficult classification of foreign objects

Active Publication Date: 2020-10-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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Problems solved by technology

[0007] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a method for detecting and classifying foreign objects on airport runways based on high-altitude and low-altitude joint scanning of UAVs, which solves the problem that the low-altitude inspection efficiency of UAVs is slow, and foreign objects appear in the image during high-altitude inspections. Too small is not easy to classify foreign objects

Method used

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  • Airfield runway foreign matter detection and classification method based on unmanned aerial vehicle high-low altitude combined scanning
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  • Airfield runway foreign matter detection and classification method based on unmanned aerial vehicle high-low altitude combined scanning

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Embodiment Construction

[0059] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0060] Such as figure 1 As shown, a method for detecting and classifying foreign objects on airport runways based on the joint scanning of UAVs at high and low altitudes includes the following steps:

[0061] S1, using the training image set of the airport runway to train the Yolo neural network model, and deploying the trained Yolo neural network model in the onboard computer of the drone;

[0062] Step S1 comprises the f...

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Abstract

The invention discloses an airport runway foreign matter detection and classification method based on unmanned aerial vehicle high-low altitude joint scanning. The method comprises the steps of training a Yolo neural network model; obtaining a fine detection area by adopting a method of combining two rounds of high-low altitude thickness detection, photographing the fine detection area, and inputting a picture into the trained Yolo neural network model to detect foreign matters and classify the foreign matters, thereby realizing accurate detection and classification of the foreign matters.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a method for detecting and classifying foreign objects on an airport runway based on high- and low-altitude joint scanning of an unmanned aerial vehicle. Background technique [0002] In the current foreign object detection method on the airport runway, there are two detection platforms, namely the fixed platform and the mobile terminal platform. [0003] The former is based on fixed laying detection and sensing devices for abnormal detection. It is characterized in that a large number of sensors such as radars and cameras are arranged on both sides of the airport runway according to the shape of the airport runway, so that the airport runway can be fully covered and sensed. However, a large number of deployments will lead to high detection costs and insufficient flexibility. Once the system is deployed, it will require a lot of additional costs to modify it. [0004] The latter...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/00G06N3/04G06N3/08
CPCG06N3/006G06N3/08G06V20/176G06N3/045G06F18/241
Inventor 杨帆裴彤李韬陈实
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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