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Unmanned aerial vehicle detection method based on deep learning

A technology of deep learning and detection methods, which is applied to computer components, instruments, character and pattern recognition, etc., can solve the problems of large manpower and material costs, and achieve the effect of facilitating control and improving detection performance

Active Publication Date: 2019-05-14
北航(四川)西部国际创新港科技有限公司
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AI Technical Summary

Problems solved by technology

Computer vision technology to detect drones requires a large number of training samples to train the detection model, and the collection of training samples will consume a lot of manpower and material costs

Method used

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  • Unmanned aerial vehicle detection method based on deep learning
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  • Unmanned aerial vehicle detection method based on deep learning

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Embodiment Construction

[0050] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and examples.

[0051] Introducing Improved SSD Algorithms

[0052] Multiscale Feature Detection

[0053] The convolutional neural network includes a downsampling layer such as a convolutional layer or a pooling layer with a step size of 2. After each time the input image passes through such a downsampling layer, the feature resolution extracted by the network is reduced to the original in the length and width directions. Half, and convolutional neural networks usually contain 5 such downsampling layers, so there are five different scales of features (such as figure 1 As shown, the left image is a larger feature map used to detect small objects (dashed box), and the right image is a smaller feature map used to detect larger objects (dashed box). Each cell in the fi...

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Abstract

The invention discloses an unmanned aerial vehicle detection method based on deep learning. The method comprises the steps of collecting data; data enhancement; constructing a deep convolutional neural network model; performing model training; perform ing model deployment; and applying the model. A cascaded convolutional neural network model is used, and a correction module is added, so that the detection performance of the model can be further improved only by carrying out a small amount of human intervention in the detection process. Through model compression, the model can run on the embedded equipment in real time. And various performance parameters of the unmanned aerial vehicle can be given by combining a database technology. The method has the advantages that the type of the unmanned aerial vehicle can be judged. A correction module is also added, so that the detection performance of the model can be further improved only by carrying out a small amount of human intervention in the detection process. The database technology is combined, various performance parameters of the unmanned aerial vehicle can be given, and control over the unmanned aerial vehicle is facilitated.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle supervision, in particular to a method for detecting unmanned aerial vehicles based on deep learning. Background technique [0002] With the opening of low-altitude areas, the rapid development of technology and the improvement of laws and regulations, the application fields of drones are more extensive, and more and more drones will participate in people's production and life. The number of drones flying at low altitudes has increased, and the potential safety hazards have gradually increased. Therefore, real-time supervision of drones is also particularly important. Detecting drones using traditional radar technology is extremely challenging because radar cannot tell if a target is a drone or not. [0003] The image of the drone can be obtained through photoelectric equipment (such as a camera), and the type and model of the drone can be judged using computer vision technology. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
Inventor 张学军黄如杨镇宇白浪
Owner 北航(四川)西部国际创新港科技有限公司
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