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公开(公告)号：US11741726B2
公开(公告)日：2023-08-29
申请号：US17494682
申请日：2021-10-05
Inventor： Yingying Li , Xiao Tan , Hao Sun
IPC: G06V20/56 , G06T7/194 , G06T7/70 , G06V20/54 , G06V10/22 , G08G1/01 , G06F18/23 , G06F18/2431 , G06F18/2413
CPC classification number: G06V20/588 , G06F18/23 , G06F18/2431 , G06F18/24137 , G06T7/194 , G06T7/70 , G06V10/22 , G06V20/54 , G08G1/0133 , G06T2207/30256
Abstract: A lane line detection method, an electronic device, and a storage medium, related to the field of artificial intelligence, and particularly related to computer vision and deep learning technologies, which can be applied to intelligent traffic scenes, are provided. The method includes: dividing an image into a foreground region and a background region; determining a solid line and a dotted line included in the foreground region; determining, according to the solid line and the dotted line comprised in the foreground region, whether a dotted-and-solid line is included in the foreground region; and determining a lane line detection result according to the solid line, the dotted line, and whether a dotted-and-solid line is comprised in the foreground region. According to the technical solution, the accuracy of lane line detection can be improved.
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公开(公告)号：US11823437B2
公开(公告)日：2023-11-21
申请号：US17658508
申请日：2022-04-08
Inventor： Xiao Tan , Xiaoqing Ye , Hao Sun
CPC classification number: G06V10/7715 , G06T3/0031 , G06V10/80 , G06V10/82 , G06V20/56
Abstract: The present disclosure provides a target detection and model training method and apparatus, a device and a storage medium, and relates to the field of artificial intelligence, and in particular, to computer vision and deep learning technologies, which may be applied to smart city and intelligent transportation scenarios. The target detection method includes: performing feature extraction processing on an image to obtain image features of a plurality of stages of the image; performing position coding processing on the image to obtain a position code of the image; obtaining detection results of the plurality of stages of a target in the image based on the image features of the plurality of stages and the position code; and obtaining a target detection result based on the detection results of the plurality of stages.
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公开(公告)号：US11694436B2
公开(公告)日：2023-07-04
申请号：US17164681
申请日：2021-02-01
Inventor： Minyue Jiang , Xiao Tan , Hao Sun , Hongwu Zhang , Shilei Wen , Errui Ding
CPC classification number: G06V20/20 , G06N3/045 , G06T7/97 , G06V20/176 , G06V20/56
Abstract: The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.
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4.发明公开公开(公告)号：US20230245429A1
公开(公告)日：2023-08-03
申请号：US18003463
申请日：2022-01-29
Inventor： Yue He , Yingying Li , Xiao Tan , Hao Sun
IPC: G06V10/774 , G06V20/56
CPC classification number: G06V10/774 , G06V20/588
Abstract: A method for training a lane line detection model includes: obtaining a plurality of road condition sample images and a plurality pieces of labeled lane line information corresponding to the plurality of road condition sample images; determining a plurality of elements corresponding to the plurality of road condition sample images and a plurality of element semantics corresponding to the plurality of elements; and obtaining the lane line detection model by training an initial artificial intelligence model based on the plurality of road condition sample images, the plurality of elements, the plurality of element semantics and the plurality pieces of labeled lane line information.
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公开(公告)号：US20230186486A1
公开(公告)日：2023-06-15
申请号：US17995752
申请日：2020-10-30
Inventor： Wei Zhang , Xiao Tan , Hao Sun , Shilei Wen , Hongwu Zhang , Errui Ding
CPC classification number: G06T7/20 , G06V10/25 , G06V10/44 , G06V10/761 , G06V10/762 , G06V20/46 , G06T9/00 , G06V2201/08 , G06T2207/30241 , G06T2207/30252
Abstract: A method for tracking vehicles includes: extracting a target image at a current moment from a video stream obtained during traveling of vehicles; performing instance segmentation on the target image to obtain detection boxes corresponding to individual vehicles in the target image; extracting, from the detection box for each vehicle, a set of pixel points corresponding to each vehicle; processing image features of each pixel point in the set of pixel points corresponding to each vehicle to determine features of each vehicle in the target image; and determining, according to the features of each vehicle in the target image and the degree of matching between the features of each vehicle in past images, movement trajectory of each vehicle in the target image, wherein the past images are n images adjacent to and before the target image in the video stream, and n is a positive integer.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20230009547A1
公开(公告)日：2023-01-12
申请号：US17933271
申请日：2022-09-19
Inventor： Xipeng Yang , Xiao Tan , Hao Sun , Errui Ding
IPC: G06V10/77 , G06V10/80 , G06V10/764 , G06V10/26
Abstract: A method for detecting an object based on a video includes: obtaining a plurality of image frames of a video to be detected; obtaining initial feature maps by extracting features of the plurality of image frames; for each two adjacent image frames of the plurality of image frames, obtaining a target feature map of a latter image frame of the two adjacent image frames by performing feature fusing on the sub-feature maps of the first target dimensions included in the initial feature map of a former image frame of the two adjacent image frames and the sub-feature maps of the second target dimensions included in the initial feature map of the latter image frame; and performing object detection on the respective target feature map of each image frame.
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公开(公告)号：US20220343603A1
公开(公告)日：2022-10-27
申请号：US17862588
申请日：2022-07-12
Inventor： Bo Ju , Xiaoqing Ye , Xiao Tan , Hao Sun
Abstract: Three-dimensional reconstruction method, three-dimensional reconstruction apparatus, device, and storage medium are provided. An implementation of the method may include: determining, based on an initial three-dimensional human body model, a target two-dimensional image corresponding to the three-dimensional human body model; semantically segmenting the target two-dimensional image, and determining semantic labels of pixels in the target two-dimensional image; determining semantic labels of skinned mesh vertices according to corresponding relationships between the skinned mesh vertices in the initial three-dimensional human body model and the pixels in the target two-dimensional image; determining target weights of the skinned mesh vertices according to the semantic labels of the skinned mesh vertices; and determining a target three-dimensional human body model according to the target weights.
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8.发明授权公开(公告)号：US12169942B2
公开(公告)日：2024-12-17
申请号：US17324174
申请日：2021-05-19
Inventor： Minyue Jiang , Xipeng Yang , Xiao Tan , Hao Sun
Abstract: A method for training an image depth estimation model. A sample environmental image, sample environmental point cloud data and sample edge information of the sample environmental image are input into a to-be-trained model; initial depth information of each of pixel points in the sample environmental image and a feature relationship between each of the pixel points and a corresponding neighboring pixel point of each of the pixel points are determined through the to-be-trained model, the initial depth information of each of the pixel points is optimized according to the feature relationship to obtain optimized depth information of each of the pixel points, and a parameter of the to-be-trained model is adjusted according to the optimized depth information to obtain the image depth estimation model.
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公开(公告)号：US11830242B2
公开(公告)日：2023-11-28
申请号：US17450261
申请日：2021-10-07
IPC: G06K9/00 , G06K9/62 , G06N3/08 , G06V10/94 , G06V20/56 , G06F18/241 , G06F18/2137 , G06F18/214 , G06F18/21 , G06V20/62
CPC classification number: G06V10/95 , G06F18/2137 , G06F18/2148 , G06F18/2178 , G06F18/241 , G06N3/08 , G06V20/56 , G06V20/625
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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公开(公告)号：US20230095114A1
公开(公告)日：2023-03-30
申请号：US17658508
申请日：2022-04-08
Inventor： Xiao Tan , Xiaoqing Ye , Hao Sun
Abstract: The present disclosure provides a target detection and model training method and apparatus, a device and a storage medium, and relates to the field of artificial intelligence, and in particular, to computer vision and deep learning technologies, which may be applied to smart city and intelligent transportation scenarios. The target detection method includes: performing feature extraction processing on an image to obtain image features of a plurality of stages of the image; performing position coding processing on the image to obtain a position code of the image; obtaining detection results of the plurality of stages of a target in the image based on the image features of the plurality of stages and the position code; and obtaining a target detection result based on the detection results of the plurality of stages.
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