CN111666959A - Vector image matching method and device - Google Patents

Vector image matching method and device Download PDF

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CN111666959A
CN111666959A CN201910166892.5A CN201910166892A CN111666959A CN 111666959 A CN111666959 A CN 111666959A CN 201910166892 A CN201910166892 A CN 201910166892A CN 111666959 A CN111666959 A CN 111666959A
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vector
reference object
matching
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刘洋
李永飞
庞颖
贺浩
屈军锁
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Xian University of Posts and Telecommunications
Rocket Force University of Engineering of PLA
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Rocket Force University of Engineering of PLA
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Abstract

The invention provides a vector image matching method and a vector image matching device, relates to the technical field of electronic information, and can solve the problem that a flight device cannot be positioned in a flight area with poor electromagnetic wave signals. The specific technical scheme is as follows: acquiring at least one basic image shot by the flying device, wherein the basic image is an image of the ground shot by the flying device from top to bottom in a top view; carrying out vector extraction on at least one basic image to obtain at least one vector image; and matching at least one vector image with a prestored reference image to determine the position area of the flying device in the reference image. The present disclosure is for use in flying device positioning.

Description

矢量图像匹配方法及装置Vector image matching method and device

技术领域technical field

本公开涉及电子信息技术领域,尤其涉及矢量图像匹配方法及装置。The present disclosure relates to the technical field of electronic information, and in particular, to a vector image matching method and device.

背景技术Background technique

随着飞行装置技术的发展,飞行装置技术应用到了很多领域,例如,航拍、运输、监测等。在飞行装置飞行过程中,通常要对飞行装置进行定位,例如,通过GPS(英文:GlobalPositioning System,GPS)定位,或者通过网络定位,但是,这些定位方式都需要保证在能够接收/发送电磁波信号的地方,在电磁波信号较差的飞行区域,无法实现对飞行装置定位。With the development of flight device technology, flight device technology has been applied to many fields, such as aerial photography, transportation, monitoring, and the like. During the flight of the flying device, it is usually necessary to locate the flying device, for example, through GPS (English: Global Positioning System, GPS) positioning, or through the network positioning, but these positioning methods need to ensure that they can receive/transmit electromagnetic wave signals. In some places, in the flight area with poor electromagnetic wave signal, it is impossible to locate the flight device.

发明内容SUMMARY OF THE INVENTION

本公开实施例提供一种矢量图像匹配方法及装置,能够解决在电磁波信号较差的飞行区域无法对飞行装置定位的问题,所述技术方案如下:Embodiments of the present disclosure provide a vector image matching method and device, which can solve the problem that the flight device cannot be positioned in a flight area with poor electromagnetic wave signals. The technical solutions are as follows:

根据本公开实施例的第一方面,提供一种矢量图像匹配方法,该方法包括:According to a first aspect of the embodiments of the present disclosure, there is provided a vector image matching method, the method comprising:

获取飞行装置拍摄的至少一个基本图像,基本图像是飞行装置从上向下以俯视视角拍摄的地面的图像;Acquiring at least one basic image taken by the flying device, where the basic image is an image of the ground taken by the flying device from top to bottom in a bird's-eye view;

将至少一个基本图像进行矢量提取得到至少一个矢量图像;Perform vector extraction on at least one basic image to obtain at least one vector image;

将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置在参考图像中的位置区域。Matching at least one vector image with a pre-stored reference image to determine the position area of the flying device in the reference image.

从基本图像中进行矢量提取得到矢量图像,利用矢量图像进行匹配,匹配更加准确,而且矢量图像匹配运算量较小,大大降低了复杂图像匹配时的运算量,在电磁波信号较差的区域,能够实现对飞行装置快速准确的定位。The vector image is obtained by extracting the vector from the basic image, and the vector image is used for matching, the matching is more accurate, and the vector image matching calculation amount is small, which greatly reduces the calculation amount of complex image matching. Realize fast and accurate positioning of the flying device.

在一个实施例中,将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置的位置区域,包括:In one embodiment, matching at least one vector image with a pre-stored reference image to determine the location area of the flying device includes:

根据目标矢量图像获取目标矢量图像中第一参照物的特征值;Obtain the feature value of the first reference object in the target vector image according to the target vector image;

将第一参照物的特征值与参考图像中至少一个第二参照物的特征值进行对比;comparing the feature value of the first reference object with the feature value of at least one second reference object in the reference image;

将至少一个第二参照物中与第一参照物的特征值相同的第二参照物确定为目标参照物;Determining at least one second reference object with the same characteristic value as the first reference object as the target reference object;

根据目标参照物在参考图像中的位置确定飞行装置在参考图像中的位置区域。The position area of the flying device in the reference image is determined according to the position of the target reference object in the reference image.

在一个实施例中,第一参照物包括目标矢量图像中的路口,第一参照物的特征值包括道路数量、毗邻路口方位、预设参考物数量中的至少一项。In one embodiment, the first reference object includes an intersection in the target vector image, and the feature value of the first reference object includes at least one of the number of roads, the orientation of adjacent intersections, and the number of preset reference objects.

在一个实施例中,将至少一个基本图像进行矢量提取得到至少一个矢量图像,包括:In one embodiment, performing vector extraction on at least one basic image to obtain at least one vector image, including:

根据预设特征在每一个基本图像中提取特征值;Extract feature values from each basic image according to preset features;

根据每一个基本图像的特征值生成对应的矢量图像。The corresponding vector image is generated according to the feature value of each basic image.

在一个实施例中,将至少一个矢量图像与预先存储的参考图像进行匹配,包括:In one embodiment, matching at least one vector image with a pre-stored reference image includes:

将至少一个矢量图像根据拍摄的时间顺序进行拼接,得到拼接图像;Stitching at least one vector image according to the time sequence of shooting to obtain a stitched image;

将拼接图像与参考图像进行匹配。Match the stitched image to the reference image.

根据本公开实施例的第二方面,提供一种矢量图像匹配装置,该矢量图像匹配装置包括:获取模块、提取模块、匹配模块;According to a second aspect of the embodiments of the present disclosure, there is provided a vector image matching device, the vector image matching device comprising: an acquisition module, an extraction module, and a matching module;

其中,获取模块,用于获取飞行装置拍摄的至少一个基本图像,基本图像是飞行装置从上向下以俯视视角拍摄的地面的图像;Wherein, the acquisition module is configured to acquire at least one basic image taken by the flying device, where the basic image is an image of the ground taken by the flying device from top to bottom from a top-down perspective;

提取模块,用于将至少一个基本图像进行矢量提取得到至少一个矢量图像;an extraction module, configured to perform vector extraction on at least one basic image to obtain at least one vector image;

匹配模块,用于将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置在参考图像中的位置区域。The matching module is used for matching at least one vector image with a pre-stored reference image to determine the position area of the flying device in the reference image.

在一个实施例中,匹配模块包括:第一特征值单元、对比单元、确定单元和位置单元;In one embodiment, the matching module includes: a first feature value unit, a comparison unit, a determination unit, and a position unit;

其中,第一特征值单元,用于根据目标矢量图像获取目标矢量图像中第一参照物的特征值;Wherein, the first feature value unit is used to obtain the feature value of the first reference object in the target vector image according to the target vector image;

对比单元,用于将第一参照物的特征值与参考图像中至少一个第二参照物的特征值进行对比;a comparison unit, configured to compare the feature value of the first reference object with the feature value of at least one second reference object in the reference image;

确定单元,用于将至少一个第二参照物中与第一参照物的特征值相同的第二参照物确定为目标参照物;a determination unit, configured to determine a second reference object with the same characteristic value as the first reference object in at least one second reference object as a target reference object;

位置单元,用于根据目标参照物在参考图像中的位置确定飞行装置在参考图像中的位置区域。The position unit is used for determining the position area of the flying device in the reference image according to the position of the target reference object in the reference image.

在一个实施例中,第一参照物包括目标矢量图像中的路口,第一参照物的特征值包括道路数量、毗邻路口方位、预设参考物数量中的至少一项。In one embodiment, the first reference object includes an intersection in the target vector image, and the feature value of the first reference object includes at least one of the number of roads, the orientation of adjacent intersections, and the number of preset reference objects.

在一个实施例中,提取模块包括第二特征值单元和矢量图像单元;In one embodiment, the extraction module includes a second feature value unit and a vector image unit;

第二特征值单元,用于根据预设特征在每一个基本图像中提取特征值;a second feature value unit for extracting feature values from each basic image according to preset features;

矢量图像单元,用于根据每一个基本图像的特征值生成对应的矢量图像。The vector image unit is used to generate a corresponding vector image according to the feature value of each basic image.

在一个实施例中,匹配模块包括拼接单元和匹配单元;In one embodiment, the matching module includes a splicing unit and a matching unit;

拼接单元,用于将至少一个矢量图像根据拍摄的时间顺序进行拼接,得到拼接图像;a splicing unit, used for splicing at least one vector image according to the time sequence of shooting to obtain a spliced image;

匹配单元,用于将拼接图像与参考图像进行匹配。The matching unit is used to match the stitched image with the reference image.

应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,并不能限制本公开。It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

附图说明Description of drawings

此处的附图被并入说明书中并构成本说明书的一部分,示出了符合本公开的实施例,并与说明书一起用于解释本公开的原理。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

图1是本公开实施例提供的一种矢量图像匹配方法的流程图;1 is a flowchart of a vector image matching method provided by an embodiment of the present disclosure;

图2是本公开实施例提供的一种矢量提取效果示意图;2 is a schematic diagram of a vector extraction effect provided by an embodiment of the present disclosure;

图3是本公开实施例提供的一种道路矢量提取网络逻辑示意图;3 is a schematic diagram of a road vector extraction network provided by an embodiment of the present disclosure;

图4是本公开实施例提供的一种第一参照物的特征示意图;FIG. 4 is a characteristic schematic diagram of a first reference object provided by an embodiment of the present disclosure;

图5是本公开实施例提供的一种矢量图像匹配装置的结构图;5 is a structural diagram of a vector image matching apparatus provided by an embodiment of the present disclosure;

图6是本公开实施例提供的一种矢量图像匹配装置的结构图;6 is a structural diagram of a vector image matching apparatus provided by an embodiment of the present disclosure;

图7是本公开实施例提供的一种矢量图像匹配装置的结构图;7 is a structural diagram of a vector image matching apparatus provided by an embodiment of the present disclosure;

图8是本公开实施例提供的一种矢量图像匹配装置的结构图。FIG. 8 is a structural diagram of a vector image matching apparatus provided by an embodiment of the present disclosure.

具体实施方式Detailed ways

这里将详细地对示例性实施例进行说明,其示例表示在附图中。下面的描述涉及附图时,除非另有表示,不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反,它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with this disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as recited in the appended claims.

本公开实施例提供一种矢量图像匹配方法,应用于矢量图像匹配装置,如图1所示,图1是本公开实施例提供的一种矢量图像匹配方法的流程图,本公开实施例提供的矢量图像匹配方法包括以下步骤:An embodiment of the present disclosure provides a vector image matching method, which is applied to a vector image matching apparatus. As shown in FIG. 1 , FIG. 1 is a flowchart of a vector image matching method provided by an embodiment of the present disclosure. The vector image matching method includes the following steps:

101、获取飞行装置拍摄的至少一个基本图像。101. Acquire at least one basic image captured by the flight device.

基本图像是飞行装置从上向下以俯视视角拍摄的地面的图像。飞行装置可以是无人机。The base image is an image of the ground taken from the top down by the flying device in a top-down view. The flying device may be a drone.

102、将至少一个基本图像进行矢量提取得到至少一个矢量图像。102. Perform vector extraction on at least one basic image to obtain at least one vector image.

在一个实施例中,将至少一个基本图像进行矢量提取得到至少一个矢量图像,包括:In one embodiment, performing vector extraction on at least one basic image to obtain at least one vector image, including:

根据预设特征在每一个基本图像中提取特征值;根据每一个基本图像的特征值生成对应的矢量图像。Extract feature values from each basic image according to preset features; generate corresponding vector images according to the feature values of each basic image.

例如,可以采用全卷积网络架构,实现对目标图像的逐像素分类,如图2所示,图2是本公开实施例提供的一种矢量提取效果示意图,判断基本图像中目标像素是否属于道路,目标像素是基本图像中的任意一个像素,如果目标像素属于道路,则将目标像素的特征值标记为1,如果目标像素不属于道路,则将目标像素的特征值标记为0,最终将基本图像经过提取特征得到特征值,再根据每一个像素的特征值生成矢量图像,矢量图像中,只显示了道路的形状。特征值也可以和矢量图像相同,也就是直接将特征值作为矢量图像。当然,图2中以提取的特征是道路为例进行说明,提取的特征也可以包含河流、建筑等,本公开对此不作限制。For example, a fully convolutional network architecture can be used to implement pixel-by-pixel classification of the target image, as shown in FIG. 2 , which is a schematic diagram of a vector extraction effect provided by an embodiment of the present disclosure to determine whether the target pixel in the basic image belongs to a road , the target pixel is any pixel in the basic image, if the target pixel belongs to the road, the eigenvalue of the target pixel is marked as 1, if the target pixel does not belong to the road, the eigenvalue of the target pixel is marked as 0, and finally the basic The image is extracted to obtain the feature value, and then a vector image is generated according to the feature value of each pixel. In the vector image, only the shape of the road is displayed. The eigenvalues can also be the same as vector images, that is, the eigenvalues are directly used as vector images. Of course, in FIG. 2 , the extracted feature is a road as an example for illustration, and the extracted feature may also include a river, a building, etc., which is not limited in the present disclosure.

进一步的,此处,以道路的矢量提取为例,通过以下三个步骤详细说明矢量提取的过程。Further, here, taking the vector extraction of a road as an example, the process of vector extraction is described in detail through the following three steps.

第一步,利用全卷积网络实现对基本图像像素级的图像分割。在全卷积框架下,通过添加不同底层特征向高层特征的跳层连接,分别针对不同的成像高度和不同成像传感器,实现网络结构的自适应调整,从而使得所设计的网络可以涵盖不同高度的空基平台。The first step is to use a fully convolutional network to achieve pixel-level image segmentation of the basic image. Under the full convolution framework, the network structure can be adaptively adjusted for different imaging heights and different imaging sensors by adding jump-layer connections from different low-level features to high-level features, so that the designed network can cover different heights. Space-based platform.

本步骤中图像分割采用的算法可以一种高度自适应的图像分割算法,以全卷积网络为基础,将高度信息、区域候选框尺寸信息转换模型嵌入分割网络;同时,将VGG16特征提取网络的conv5_3通过反卷积与conv4_3层特征图进行通道串联融合得到的特征图代替原有的conv4_3特征图,再与fc7、conv7_2进行通道串联操作,进一步丰富特征图的语义信息,提高检测的精度,具体网络结构如图3所示。图3中,左侧为输入的图像和高度信息,考虑到实际运用,这里以1024×768大小的图像为例,经过VGG16特征提取网络将conv5_3特征层进行一次2×2的反卷积运算与conv4_3通道串联融合,得到融合后的特征图,然后与fc7、conv7_2分别进行一次卷积运算,将三个不同的特征图通道串联后,进行一次1×1的卷积运算和归一化,得到通道数为512,大小为38×38的特征图。上述模型中,检测器部分依旧采用的是FSSD的基本结构,通过对得到的特征图进行多次卷积运算,生成6个感受野和大小都不相同的特征图。The algorithm used for image segmentation in this step can be a highly adaptive image segmentation algorithm, which is based on a fully convolutional network and embeds the conversion model of height information and region candidate frame size information into the segmentation network; at the same time, the VGG16 feature extraction network The feature map obtained by conv5_3 through deconvolution and the channel series fusion of the conv4_3 layer feature map replaces the original conv4_3 feature map, and then performs channel series operation with fc7 and conv7_2 to further enrich the semantic information of the feature map and improve the detection accuracy. The network structure is shown in Figure 3. In Figure 3, the left side is the input image and height information. Considering the actual application, here is an image with a size of 1024 × 768 as an example. After the VGG16 feature extraction network, the conv5_3 feature layer is subjected to a 2 × 2 deconvolution operation and The conv4_3 channels are fused in series to obtain the fused feature map, and then a convolution operation is performed with fc7 and conv7_2 respectively. After connecting the three different feature map channels in series, a 1×1 convolution operation and normalization are performed to obtain The number of channels is 512 and the feature map size is 38×38. In the above model, the detector part still adopts the basic structure of FSSD. By performing multiple convolution operations on the obtained feature maps, six feature maps with different receptive fields and sizes are generated.

该网络结构通过卷积网络编码特征,然后通过反卷积和上采样解码,属于一种具有成像高度自适应能力的深度编解码网络。通过对网络的深度进行调整,降低网络深度,获取更高的道路细节分割精度。The network structure encodes features through a convolutional network, and then decodes through deconvolution and upsampling, which belongs to a deep encoder-decoder network with highly adaptive imaging capabilities. By adjusting the depth of the network, the network depth is reduced to obtain higher segmentation accuracy of road details.

例如,可以将空对地成像拍摄图像进行规范化(假设为512×512的大小),采用全卷积网路,在若干层卷积和若干次最大池化后,便可以完成对该空对地图像的编码。由于经过了卷积和池化,所得到的特征图通常会远小于原图(假设为1/4)。为了实现对图像的像素级分割,将特征图作为解码网络的输入层,在完成若干次上采样后,得到和原图大小一致的特征图。最后,将和原图大小一致的特征图进行一次特殊卷积,该卷积核大小为1×1的卷积层,将输出映射成为像素分类的概率图,其大小为512×512×1。至此,便完成了道路像素级分割网络的设计。For example, the image captured by air-to-ground imaging can be normalized (assuming a size of 512×512), using a fully convolutional network, after several layers of convolution and several max pooling, the air-to-ground image can be completed. image encoding. Due to convolution and pooling, the resulting feature map is usually much smaller than the original image (assuming 1/4). In order to achieve pixel-level segmentation of the image, the feature map is used as the input layer of the decoding network. After several upsampling, the feature map with the same size as the original image is obtained. Finally, a special convolution is performed on the feature map with the same size as the original image. The convolution kernel size is a convolutional layer of 1×1, and the output is mapped into a probability map of pixel classification with a size of 512×512×1. So far, the design of the road pixel-level segmentation network is completed.

第二步,制作道路分割数据集并进行网络参数训练。设定空对地道路分割数据集,主要包括卫星成像数据集、无人机航拍数据集等,通过对原始数据进行道路标注,形成具有一定规模的、涵盖不同成像高度的空对地道路分割数据集。然后,在该数据集的基础上,对前述设计的全卷积网络进行训练,得到高精度网络权重,将该权值矩阵送入机载计算平台。The second step is to create a road segmentation dataset and train network parameters. Set the air-to-ground road segmentation data set, mainly including satellite imaging data sets, UAV aerial photography data sets, etc., and form air-to-ground road segmentation data with a certain scale and covering different imaging heights by labeling the original data. set. Then, on the basis of the data set, the fully convolutional network designed above is trained to obtain high-precision network weights, and the weight matrix is sent to the airborne computing platform.

第三步,基于道路提取网络的实际飞行图像道路自动提取。将道路提取网络结构及其权重文件送入机载运算处理平台后,由空基飞行平台进行飞行拍摄和道路自动提取,实时得到当前时刻无人机位置所对应的道路拓扑结构,并以矢量图的形式进行描述和存储。The third step is automatic road extraction based on the actual flight image of the road extraction network. After the road extraction network structure and its weight files are sent to the airborne computing and processing platform, the air-based flight platform performs flight photography and automatic road extraction, and obtains the road topology structure corresponding to the current position of the UAV in real time. are described and stored in the form of

103、将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置在参考图像中的位置区域。103. Match at least one vector image with a pre-stored reference image to determine a position area of the flying device in the reference image.

在一个实施例中,参考图像是飞行装置所飞行的地区的矢量地图,也就是按照步骤101和和步骤102,将飞行装置所飞行的地区进行拍摄和矢量提取,然后进行拼接得到整个地区的矢量图像。In one embodiment, the reference image is a vector map of the area where the flying device flies, that is, according to steps 101 and 102, the area where the flying device is flying is photographed and the vector is extracted, and then the vector of the entire area is obtained by splicing image.

在一个实施例中,将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置的位置区域,包括:In one embodiment, matching at least one vector image with a pre-stored reference image to determine the location area of the flying device includes:

根据目标矢量图像获取目标矢量图像中第一参照物的特征值;将第一参照物的特征值与参考图像中至少一个第二参照物的特征值进行对比;将至少一个第二参照物中与第一参照物的特征值相同的第二参照物确定为目标参照物;根据目标参照物在参考图像中的位置确定飞行装置在参考图像中的位置区域。Obtain the feature value of the first reference object in the target vector image according to the target vector image; compare the feature value of the first reference object with the feature value of at least one second reference object in the reference image; compare the at least one second reference object with The second reference object with the same feature value of the first reference object is determined as the target reference object; the position area of the flying device in the reference image is determined according to the position of the target reference object in the reference image.

目标矢量图像是至少一个矢量图像中任意一个,第一参照物是目标矢量图像中任意一个参照物,此处只是以第一参照物为例进行说明,不代表任何限制。进一步的,在一个实施例中,第一参照物包括目标矢量图像中的路口,第一参照物的特征值包括道路数量、毗邻路口方位、预设参考物数量中的至少一项。The target vector image is any one of at least one vector image, and the first reference object is any one of the reference objects in the target vector image. Here, the first reference object is only used as an example for description, and does not represent any limitation. Further, in one embodiment, the first reference object includes an intersection in the target vector image, and the feature value of the first reference object includes at least one of the number of roads, the orientation of adjacent intersections, and the number of preset reference objects.

例如,以第一参照物和第二参照物是路口为例,如图4所示,图4是本公开实施例提供的一种第一参照物的特征示意图,图4中,第一参照物(即目标矢量图像中的某一个路口)的特征值包括道路数量和毗邻路口方位;道路数量即为与该路口连通的道路的数量,毗邻路口方位指的是与该路口距离最近的路口相对于该路口的方位,如图4所示,距离该路口最近的路口位于正东方(右边),方位从东边开始,按照顺时针方向依次排列进行编号,例如,东方标记为1,东南方标记为2,南方标记为3,西南方标记为4,西方标记为5,西北方标记为6,北方标记为7,东北方标记为8,如图3所示的路口,距离该路口最近的路口在东方,所以记为1,则该路口的特征值为[4,1]。根据特征值在参考图像的至少一个第二参照物中确定特征值相同的第二参照物作为目标参照物,即可确定目标矢量图像在参考图像中的位置区域。For example, taking the first reference object and the second reference object being an intersection as an example, as shown in FIG. 4 , FIG. 4 is a schematic diagram of the characteristics of a first reference object provided by an embodiment of the present disclosure. (that is, a certain intersection in the target vector image) The feature value includes the number of roads and the orientation of adjacent intersections; the number of roads is the number of roads connected to the intersection, and the orientation of adjacent intersections refers to the relative distance of the intersection with the closest distance to the intersection. The orientation of the intersection, as shown in Figure 4, the closest intersection to the intersection is located in the due east (right), the orientation starts from the east, and is numbered in clockwise order, for example, the east is marked as 1, and the southeast is marked as 2 , the south is marked as 3, the southwest is marked as 4, the west is marked as 5, the northwest is marked as 6, the north is marked as 7, and the northeast is marked as 8. The intersection as shown in Figure 3, the closest intersection to the intersection is in the east , so it is recorded as 1, then the eigenvalue of the intersection is [4,1]. The second reference object with the same feature value is determined as the target reference object in at least one second reference object of the reference image according to the feature value, and the position area of the target vector image in the reference image can be determined.

在一个实施例中,将至少一个矢量图像与预先存储的参考图像进行匹配,包括:In one embodiment, matching at least one vector image with a pre-stored reference image includes:

将至少一个矢量图像根据拍摄的时间顺序进行拼接,得到拼接图像;将拼接图像与参考图像进行匹配。The at least one vector image is stitched according to the time sequence of shooting to obtain a stitched image; the stitched image is matched with the reference image.

本公开实施例提供的矢量图像匹配方法,从基本图像中进行矢量提取得到矢量图像,利用矢量图像进行匹配,匹配更加准确,而且矢量图像匹配运算量较小,大大降低了复杂图像匹配时的运算量,在电磁波信号较差的区域,能够实现对飞行装置快速准确的定位。In the vector image matching method provided by the embodiments of the present disclosure, a vector image is obtained by extracting a vector from a basic image, and the vector image is used for matching, so that the matching is more accurate, and the vector image matching operation amount is small, which greatly reduces the operation in complex image matching. It can realize fast and accurate positioning of the flying device in the area with poor electromagnetic wave signal.

基于上述图1对应的实施例种所描述的矢量图像匹配方法,本公开实施例提供一种矢量图像匹配装置,用于执行上述图1对应的实施例中所描述的矢量图像匹配方法,如图5所示,该矢量图像匹配装置50包括:获取模块501、提取模块502、匹配模块503;Based on the vector image matching method described in the above-mentioned embodiment corresponding to FIG. 1 , an embodiment of the present disclosure provides a vector image matching apparatus for executing the vector image matching method described in the above-mentioned embodiment corresponding to FIG. 1 , as shown in FIG. 5, the vector image matching device 50 includes: an acquisition module 501, an extraction module 502, and a matching module 503;

其中,获取模块501,用于获取飞行装置拍摄的至少一个基本图像,基本图像是飞行装置从上向下以俯视视角拍摄的地面的图像;Wherein, the acquiring module 501 is configured to acquire at least one basic image taken by the flying device, where the basic image is an image of the ground taken by the flying device from top to bottom from a top-down perspective;

提取模块502,用于将至少一个基本图像进行矢量提取得到至少一个矢量图像;an extraction module 502, configured to perform vector extraction on at least one basic image to obtain at least one vector image;

匹配模块503,用于将至少一个矢量图像与预先存储的参考图像进行匹配,确定飞行装置在参考图像中的位置区域。The matching module 503 is configured to match at least one vector image with a pre-stored reference image to determine the position area of the flying device in the reference image.

在一个实施例中,如图6所示,匹配模块503包括:第一特征值单元5031、对比单元5032、确定单元5033和位置单元5034;In one embodiment, as shown in FIG. 6 , the matching module 503 includes: a first feature value unit 5031, a comparison unit 5032, a determination unit 5033 and a position unit 5034;

其中,第一特征值单元5031,用于根据目标矢量图像获取目标矢量图像中第一参照物的特征值;Wherein, the first feature value unit 5031 is used to obtain the feature value of the first reference object in the target vector image according to the target vector image;

对比单元5032,用于将第一参照物的特征值与参考图像中至少一个第二参照物的特征值进行对比;a comparison unit 5032, configured to compare the feature value of the first reference object with the feature value of at least one second reference object in the reference image;

确定单元5033,用于将至少一个第二参照物中与第一参照物的特征值相同的第二参照物确定为目标参照物;a determining unit 5033, configured to determine a second reference object having the same characteristic value as the first reference object in at least one second reference object as a target reference object;

位置单元5034,用于根据目标参照物在参考图像中的位置确定飞行装置在参考图像中的位置区域。The position unit 5034 is configured to determine the position area of the flying device in the reference image according to the position of the target reference object in the reference image.

在一个实施例中,第一参照物包括目标矢量图像中的路口,第一参照物的特征值包括道路数量、毗邻路口方位、预设参考物数量中的至少一项。In one embodiment, the first reference object includes an intersection in the target vector image, and the feature value of the first reference object includes at least one of the number of roads, the orientation of adjacent intersections, and the number of preset reference objects.

在一个实施例中,如图7所示,提取模块502包括第二特征值单元5021和矢量图像单元5022;In one embodiment, as shown in FIG. 7 , the extraction module 502 includes a second feature value unit 5021 and a vector image unit 5022;

第二特征值单元5021,用于根据预设特征在每一个基本图像中提取特征值;The second feature value unit 5021 is used to extract feature values from each basic image according to preset features;

矢量图像单元5022,用于根据每一个基本图像的特征值生成对应的矢量图像。The vector image unit 5022 is configured to generate a corresponding vector image according to the feature value of each basic image.

在一个实施例中,如图8所示,匹配模块503包括拼接单元5035和匹配单元5036;In one embodiment, as shown in FIG. 8 , the matching module 503 includes a splicing unit 5035 and a matching unit 5036;

拼接单元5035,用于将至少一个矢量图像根据拍摄的时间顺序进行拼接,得到拼接图像;The splicing unit 5035 is used for splicing at least one vector image according to the time sequence of shooting to obtain a spliced image;

匹配单元5036,用于将拼接图像与参考图像进行匹配。A matching unit 5036, configured to match the stitched image with the reference image.

本公开实施例提供的矢量图像匹配装置,从基本图像中进行矢量提取得到矢量图像,利用矢量图像进行匹配,匹配更加准确,而且矢量图像匹配运算量较小,大大降低了复杂图像匹配时的运算量,在电磁波信号较差的区域,能够实现对飞行装置快速准确的定位。The vector image matching device provided by the embodiment of the present disclosure obtains a vector image by extracting a vector from a basic image, and uses the vector image to perform matching, the matching is more accurate, and the vector image matching calculation amount is small, which greatly reduces the calculation of complex image matching. It can realize fast and accurate positioning of the flying device in the area with poor electromagnetic wave signal.

基于上述图1对应的实施例中所描述的矢量图像匹配方法,本公开实施例还提供一种计算机可读存储介质,例如,非临时性计算机可读存储介质可以是只读存储器(英文:Read Only Memory,ROM)、随机存取存储器(英文:Random Access Memory,RAM)、CD-ROM、磁带、软盘和光数据存储装置等。该存储介质上存储有计算机指令,用于执行上述图1对应的实施例中所描述的矢量图像匹配方法,此处不再赘述。Based on the vector image matching method described in the above-mentioned embodiment corresponding to FIG. 1 , an embodiment of the present disclosure further provides a computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be a read-only memory (English: Read Only Memory, ROM), random access memory (English: Random Access Memory, RAM), CD-ROM, magnetic tape, floppy disk and optical data storage device, etc. The storage medium stores computer instructions for executing the vector image matching method described in the embodiment corresponding to FIG. 1 , which will not be repeated here.

本领域技术人员在考虑说明书及实践这里公开的公开后,将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化,这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的,本公开的真正范围和精神由下面的权利要求指出。Other embodiments of the present disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not disclosed by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

Claims (10)

1. A vector image matching method, the method comprising:
acquiring at least one basic image shot by a flying device, wherein the basic image is an image of the ground shot by the flying device from top to bottom in a top view;
performing vector extraction on the at least one basic image to obtain at least one vector image;
and matching the at least one vector image with a prestored reference image to determine the position area of the flying device in the reference image.
2. The method of claim 1, wherein matching the at least one vector image to a pre-stored reference image to determine the location area of the flying device comprises:
acquiring a characteristic value of a first reference object in a target vector image according to the target vector image;
comparing the characteristic value of the first reference object with the characteristic value of at least one second reference object in the reference image;
determining a second reference object which is the same as the characteristic value of the first reference object in the at least one second reference object as a target reference object;
and determining the position area of the flying device in the reference image according to the position of the target reference object in the reference image.
3. The method of claim 2,
the first reference object comprises an intersection in the target vector image, and the characteristic value of the first reference object comprises at least one of the number of roads, the position of the adjacent intersection and the number of preset reference objects.
4. The method of claim 1, wherein vector extracting the at least one base image to obtain at least one vector image comprises:
extracting a characteristic value in each basic image according to preset characteristics;
and generating a corresponding vector image according to the characteristic value of each basic image.
5. The method according to any of claims 1-4, wherein matching the at least one vector image with a pre-stored reference image comprises:
splicing the at least one vector image according to the shooting time sequence to obtain a spliced image;
and matching the spliced image with the reference image.
6. A vector image matching apparatus, characterized in that the vector image matching apparatus comprises: the device comprises an acquisition module, an extraction module and a matching module;
the acquisition module is used for acquiring at least one basic image shot by a flying device, wherein the basic image is an image of the ground shot by the flying device from top to bottom in a top view;
the extraction module is used for carrying out vector extraction on the at least one basic image to obtain at least one vector image;
the matching module is used for matching the at least one vector image with a prestored reference image and determining the position area of the flying device in the reference image.
7. The apparatus of claim 6, wherein the matching module comprises: the device comprises a first characteristic value unit, a comparison unit, a determination unit and a position unit;
the first characteristic value unit is used for acquiring a characteristic value of a first reference object in a target vector image according to the target vector image;
the comparison unit is used for comparing the characteristic value of the first reference object with the characteristic value of at least one second reference object in the reference image;
the determining unit is used for determining a second reference object which is the same as the characteristic value of the first reference object in the at least one second reference object as a target reference object;
the position unit is used for determining the position area of the flying device in the reference image according to the position of the target reference object in the reference image.
8. The apparatus of claim 7,
the first reference object comprises an intersection in the target vector image, and the characteristic value of the first reference object comprises at least one of the number of roads, the position of the adjacent intersection and the number of preset reference objects.
9. The apparatus of claim 6, wherein the extraction module comprises a second feature value unit and a vector image unit;
the second characteristic value unit is used for extracting a characteristic value in each basic image according to preset characteristics;
and the vector image unit is used for generating a corresponding vector image according to the characteristic value of each basic image.
10. The apparatus according to any one of claims 6-9, wherein the matching module comprises a splicing unit and a matching unit;
the splicing unit is used for splicing the at least one vector image according to a shooting time sequence to obtain a spliced image;
the matching unit is used for matching the spliced image with the reference image.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113535996A (en) * 2021-05-27 2021-10-22 中国人民解放军火箭军工程大学 A method and device for preparing road image data set based on aerial imagery

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102706352A (en) * 2012-05-21 2012-10-03 南京航空航天大学 Vector map matching navigation method for linear target in aviation
CN104075710A (en) * 2014-04-28 2014-10-01 中国科学院光电技术研究所 Maneuvering extension target axial attitude real-time estimation method based on track prediction
JP2016134136A (en) * 2015-01-22 2016-07-25 キャンバスマップル株式会社 Image processing apparatus, and image processing program
CN105868772A (en) * 2016-03-23 2016-08-17 百度在线网络技术(北京)有限公司 Image identification method and apparatus
CN108168522A (en) * 2017-12-11 2018-06-15 宁波亿拍客网络科技有限公司 A kind of unmanned plane observed object method for searching and correlation technique again
CN108513642A (en) * 2017-07-31 2018-09-07 深圳市大疆创新科技有限公司 A kind of image processing method, unmanned plane, ground control cabinet and its image processing system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102706352A (en) * 2012-05-21 2012-10-03 南京航空航天大学 Vector map matching navigation method for linear target in aviation
CN104075710A (en) * 2014-04-28 2014-10-01 中国科学院光电技术研究所 Maneuvering extension target axial attitude real-time estimation method based on track prediction
JP2016134136A (en) * 2015-01-22 2016-07-25 キャンバスマップル株式会社 Image processing apparatus, and image processing program
CN105868772A (en) * 2016-03-23 2016-08-17 百度在线网络技术(北京)有限公司 Image identification method and apparatus
CN108513642A (en) * 2017-07-31 2018-09-07 深圳市大疆创新科技有限公司 A kind of image processing method, unmanned plane, ground control cabinet and its image processing system
CN108168522A (en) * 2017-12-11 2018-06-15 宁波亿拍客网络科技有限公司 A kind of unmanned plane observed object method for searching and correlation technique again

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113535996A (en) * 2021-05-27 2021-10-22 中国人民解放军火箭军工程大学 A method and device for preparing road image data set based on aerial imagery
CN113535996B (en) * 2021-05-27 2023-08-04 中国人民解放军火箭军工程大学 A road image data set preparation method and device based on aerial images

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