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Unmanned aerial vehicle airborne platform-based vehicle type identification method

A technology for vehicle identification and unmanned aerial vehicles, which is applied in the field of video image technology and intelligent transportation, can solve the problems of misclassification of visibility, lack of flexibility, and fixed installation, and achieves accurate and reliable classification results, wide application range, and reduced detection. cost effect

Inactive Publication Date: 2016-11-09
CHONGQING UNIV
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AI Technical Summary

Problems solved by technology

[0004] However, the current image processing vehicle identification technology is mostly based on the principle of color image recognition, which is prone to misclassification at night, in bad weather conditions, and low visibility.
In addition, the camera equipment used to collect images is usually fixed and lacks flexibility

Method used

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Embodiment Construction

[0017] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] figure 1 It is a flowchart of the method of the present invention, the method may further comprise the steps:

[0019] S1: Preprocess the spatial position of the UAV airborne platform and adjust it to a detectable height range. The specific steps of UAV spatial position adjustment are as follows:

[0020] S11: Use GPS positioning technology to obtain the height H of the drone to the ground;

[0021] S12: Judging whether the current spatial height of the drone satisfies formula H min ≤H≤H max , if it is, the vehicle target detection is performed, otherwise, the spatial position of the UAV is adjusted to meet the above spatial constraints.

[0022] S2: Collect positive and negative samples of infrared images for vehicle target detection offline, preprocess the samples respectively, use convolutional neural network (CNN) to perform...

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Abstract

The invention discloses an unmanned aerial vehicle airborne platform-based vehicle type identification method and belongs to the technical field of video images. The method comprises the following steps of S1: adjusting the height of an unmanned aerial vehicle above the ground to a position suitable for vehicle type identification; S2: acquiring infrared image positive and negative samples used for vehicle target detection offline, performing characteristic extraction by utilizing a convolutional neural network (CNN), and performing support vector machine (SVM) training to obtain an SVM classifier model used for online vehicle type identification; S3: performing infrared photography by utilizing an infrared video camera to obtain a video image signal; and S4: performing sliding window sampling on a shot infrared image sequence, extracting vehicle type characteristics by utilizing the CNN, and inputting the characteristics in the classifier model obtained in the step S2 to perform classification. According to the method, the vehicle type identification can be finished in the day, at night and under the condition of severe weather condition and relatively low visibility by adopting the infrared video camera; and meanwhile, the method can be used for dynamically selecting a detection region, so that the flexibility of a detection system is improved.

Description

technical field [0001] The invention belongs to the field of video image technology and intelligent transportation technology, and relates to a vehicle identification method based on an unmanned aerial vehicle (UAV) airborne platform. Background technique [0002] With the continuous improvement of living standards in modern society, automobiles, as an important means of transportation, show a trend of rapid growth in number, which brings great challenges to traffic supervision. Although the rapid increase in the number of cars has brought a lot of convenience to people's lives, it has also brought a series of traffic problems such as running red lights, traffic jams, speeding, and traffic accidents. Traditional road traffic management methods relying on manpower or basic transportation facilities can no longer meet the current development needs. In recent years, technologies such as computer technology, artificial intelligence and pattern recognition have been vigorously de...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/584G06F18/2411
Inventor 尹宏鹏柴毅李天柱陈波王唯
Owner CHONGQING UNIV
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