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公开(公告)号：US20190095730A1
公开(公告)日：2019-03-28
申请号：US16136128
申请日：2018-09-19
Inventor： Huiyuan FU , Huadong MA , Yifan ZHANG , Yu CAO , Dengkui ZHANG
Abstract: Embodiments of the present invention provide an end-to-end lightweight method and apparatus for license plate recognition. The method comprises: obtaining an image to be recognized; obtaining a number of a license plate in the image to be recognized and position coordinates of the license plate in the image to be recognized on the basis of the image to be recognized and a pre-trained target license plate recognition model, wherein the target license plate recognition model comprises a target feature extraction network, a target region candidate localization network, a target super-resolution generation network and a target recurrent neural network. Because in this solution, once an image to be recognized is input into the target license plate recognition model, the target license plate recognition model can output the license plate number and position coordinates of the license plate in the image to be recognized, one realizes an end-to-end model. The model has relatively strong robustness, and it can detect and recognize pictures taken under different camera angles. Moreover, computation variables such as image features can be reused without repeated computations, the model takes up less RAM and the speed of license plate recognition is greatly improved.
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2.发明申请公开(公告)号：US20160334546A1
公开(公告)日：2016-11-17
申请号：US14845859
申请日：2015-09-04
Inventor： Huadong MA , Huiyuan FU , Zheng ZHANG
CPC classification number: G01W1/00 , G06K9/00664 , G06K9/4642 , G06K9/6269
Abstract: The embodiments of the present invention disclose a weather recognition method and device based on image information detection, which includes: obtaining an image to be detected; extracting multiple first image features of the image to be detected with respect to each preset type of weather according to a number of first preset algorithms preset correspondingly for different preset types of weather; inputting the extracted multiple first image features to a preset multi-kernel classifier, the multi-kernel classifier performing classification according to the inputted image features to identify the weather in which the image to be detected was taken; wherein the multi-kernel classifier is a classifier for the preset types of weather realized by: selecting a first preset number of image samples for the different preset types of weather in which the image was taken respectively; and for the image samples of each preset type of weather respectively, extracting the first image features of each image sample according to the first preset algorithm which corresponds to this preset type of weather; and performing machine learning for the extracted first image features according to a preset multi-kernel learning algorithm. The weather in which the image was taken can be identified by applying the solutions provided by the embodiments of the present invention.
Abstract translation: 本发明的实施例公开了一种基于图像信息检测的天气识别方法和装置，其包括：获得要检测的图像; 根据针对不同预设类型的天气预先相应地设定的第一预设算法的数量，提取相对于每种预设类型的天气的要检测的图像的多个第一图像特征; 将所提取的多个第一图像特征输入到预设的多内核分类器中，所述多内核分类器根据所输入的图像特征进行分类，以识别拍摄所述图像的天气; 其中所述多内核分类器是通过以下方式实现的预设类型的天气的分类器：为分别拍摄图像的不同预设类型的天气选择第一预设数量的图像样本; 并分别针对每种预设类型的天气的图像样本，根据与该预设类型的天气相对应的第一预设算法提取每个图像样本的第一图像特征; 并根据预设的多核学习算法对所提取的第一图像特征执行机器学习。 拍摄图像的天气可以通过应用本发明实施例提供的解决方案来识别。
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公开(公告)号：US20180060684A1
公开(公告)日：2018-03-01
申请号：US15350813
申请日：2016-11-14
Inventor： Huadong MA , Wu LIU , Xinchen LIU , Haitao ZHANG , Huiyuan FU
CPC classification number: G06K9/325 , G06F17/30253 , G06F17/30256 , G06K9/00771 , G06K9/6215
Abstract: The present application discloses a vehicle searching method and device, which can perform the steps of: calculating an appearance similarity distance between a first image of a target vehicle and several second images containing the searched vehicle; selecting several images from the several second images as several third images; obtaining corresponding license plate features of license plate areas in the first image and each of the third images with a preset Siamese neural network model; calculating a license plate feature similarity distance between the first image and each of the third images according to license plate feature; calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; obtaining a the first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances. The solution provided by the present application is not limited by application scenes, and it also improves vehicle searching speed and accuracy while reducing requirements of hardware such as cameras that collect images of a vehicle and auxiliary devices.
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