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公开(公告)号：US20210312650A1
公开(公告)日：2021-10-07
申请号：US17353634
申请日：2021-06-21
Inventor： Xiaoqing YE , Hao SUN
Abstract: The present disclosure provides a method of training a depth estimation network, which relates to fields of computer vision, deep learning, and image processing technology. The method includes: performing a depth estimation on an original image by using a depth estimation network, so as to obtain a depth image for the original image; removing a moving object from the original image so as to obtain a preprocessed image for the original image; estimating a pose based on the original image and modifying the pose based on the preprocessed image; and adjusting parameters of the depth estimation network according to the original image, the depth image and the pose modified. The present disclosure further provides an apparatus of training a depth estimation network, a method and apparatus of estimating a depth of an image, an electronic device, and a storage medium.
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公开(公告)号：US20230102467A1
公开(公告)日：2023-03-30
申请号：US17956393
申请日：2022-09-29
IPC: G06V10/25 , G06V10/44 , G06V10/764 , G06V10/771 , G06V10/82
Abstract: A method of detecting an image, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a smart city and an intelligent cloud. The method includes: performing a feature extraction on an image to be detected, so as to obtain a feature map of the image to be detected; generating a prediction box in the feature map according to the feature map; generating a mask for the prediction box according to a key region of a target object; and classifying the prediction box using the mask as a classification enhancement information, so as to obtain a category of the prediction box.
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公开(公告)号：US20230027813A1
公开(公告)日：2023-01-26
申请号：US17936570
申请日：2022-09-29
Inventor： Xipeng YANG , Xiao TAN , Hao SUN , Errui DING
Abstract: An object detecting method includes: obtaining an object image of an object; obtaining an object feature map by performing feature extraction on the object image; obtaining decoded features by performing feature mapping on the object feature map by adopting a mapping network of an object recognition model; obtaining positions of prediction boxes by inputting the decoded features into a first prediction layer of the object recognition model to perform object regression prediction; and obtaining classes of objects within the prediction boxes by inputting the decoded features into a second prediction layer of the object recognition model to perform object class prediction.
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24.发明申请公开(公告)号：US20230005272A1
公开(公告)日：2023-01-05
申请号：US17944742
申请日：2022-09-14
Inventor： Yingying LI , Xiao TAN , Hao SUN
Abstract: The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.
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公开(公告)号：US20220392204A1
公开(公告)日：2022-12-08
申请号：US17891381
申请日：2022-08-19
IPC: G06V10/774 , G06V10/776
Abstract: A method of training a model, an electronic device, and a readable storage medium are provided, which relate to a field of artificial intelligence, in particular to computer vision and deep learning technologies, and specifically used in smart city and intelligent transportation scenarios. The method includes: determining a target pre-trained model; and performing an unsupervised training and/or a semi-supervised training on the target pre-trained model based on an image acquired by the target terminal, so as to obtain a first target trained model.
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Patent Agency Ranking
26.发明申请公开(公告)号：US20220375118A1
公开(公告)日：2022-11-24
申请号：US17880931
申请日：2022-08-04
Inventor： Yingying LI , Xinyi DAI , Xiao TAN , Hao SUN
Abstract: Provided is a method and apparatus for identifying a vehicle cross-line. The method may include: determining, in each road condition image of a plurality of road condition images, position information of a target lane line and position information of a target vehicle; determining, based on the position information of the target lane line and the position information of the target vehicle, a relative positional relationship between the target vehicle and the target lane line corresponding to the each road condition image; and determining that the target vehicle crosses the line, if the relative positional relationships corresponding to the plurality of road condition images meet a preset condition.
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公开(公告)号：US20220351493A1
公开(公告)日：2022-11-03
申请号：US17868630
申请日：2022-07-19
Inventor： Xiangbo SU , Qiman Wu , Shuai KANG , Jian WANG , Hao SUN
Abstract: A method and apparatus for detecting an object. The method includes: inputting a to-be-detected picture into a target detection model, marking at least one region of interest in the picture using the target detection model, and determining an initial confidence that each region of interest contains a preset target object; determining concentration information of an interferent in the picture; and determining, based on the concentration information and the initial confidence corresponding to the region of interest, a target confidence that the region of interest contains the preset target object.
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公开(公告)号：US20220222941A1
公开(公告)日：2022-07-14
申请号：US17707657
申请日：2022-03-29
Inventor： Desen ZHOU , Jian WANG , Hao SUN
Abstract: A method for recognizing an action includes: obtaining a sequence for key points; extracting first space-time features corresponding to the sequence; obtaining a second space-time feature corresponding to a time granularity by performing feature extraction on the first space-time features based on the time granularity; and obtaining a target recognized action of the sequence based on second space-time features corresponding to time granularities.
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29.发明申请公开(公告)号：US20220139061A1
公开(公告)日：2022-05-05
申请号：US17576198
申请日：2022-01-14
Inventor： Jian WANG , Zipeng LU , Hao SUN , Zhiyong JIN , Errui DING
Abstract: Provided are a training method and apparatus for a human keypoint positioning model, a human keypoint positioning method and apparatus, a device, a medium and a program product. The training method includes determining an initial positioned point of each of keypoints; acquiring N candidate points of each keypoint according to a position of the initial positioned point; extracting a first feature image, and forming N sets of graph structure feature data according to the first feature image and the N candidate points; performing graph convolution on the N sets of graph structure feature data to obtain N sets of offsets; correcting initial positioned points of all the keypoints to obtain N sets of current positioning results; and calculating each set of loss values according to labeled true values of all the keypoints and each set of current positioning results, and performing supervised training on the positioning model.
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公开(公告)号：US20220027651A1
公开(公告)日：2022-01-27
申请号：US17450261
申请日：2021-10-07
Abstract: A method for generating a license plate defacement classification model, a license plate defacement classification method an electronic device and a storage medium, and related to the technical field of artificial intelligence, and specifically, to the technical field of computer vision and the technical field of intelligent transportation are provided. The method for generating a license plate defacement classification model includes: acquiring training data, wherein the training data includes a plurality of annotated vehicle images, annotated content includes information indicating that a license plate is defaced or is not defaced, and the annotated content further includes location information of a license plate area; and training a first neural network by using the training data, to obtain the license plate defacement classification model for predicting whether the license plate in a target vehicle image is defaced. A robust license plate defacement classification model can be obtained by using embodiments.
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