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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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公开(公告)号：US20230068238A1
公开(公告)日：2023-03-02
申请号：US18049326
申请日：2022-10-25
Inventor： Yingying Li , Xiao Tan , Hao Sun
Abstract: A method for processing an image includes obtaining an image to be processed; obtaining a depth feature map by inputting the image to be processed into a depth feature extraction network in an image recognition model, and obtaining a semantic segmentation feature map by inputting the image to be processed into a semantic feature extraction network of the image recognition model; obtaining a target depth feature map fused with semantic features and a target semantic segmentation feature map fused with depth features by inputting the depth feature map and the semantic segmentation feature map into a feature interaction network of the recognition model for fusion; and obtaining a depth estimation result and a semantic segmentation result by inputting the target depth feature map and the target semantic segmentation feature map into a corresponding output network in the recognition model.
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公开(公告)号：US20230017578A1
公开(公告)日：2023-01-19
申请号：US17935712
申请日：2022-09-27
Inventor： Xiangbo Su , Jian Wang , Hao Sun
IPC: G06T7/70 , G06V10/44 , G06V10/764 , G06V10/771 , G06V10/80
Abstract: An image processing and model training methods, an electronic device, and a storage medium are provided, and relate to the technical field of artificial intelligence, and in particular to the technical fields of computer vision and deep learning, which can be specifically applied to smart cities and intelligent cloud scenes. The image processing method includes: obtaining at least one first feature map of an image to be processed, wherein feature data of a target pixel in the first feature map is generated according to the target pixel and another pixel within a set range around the target pixel; determining a classification to which the target pixel belongs according to the feature data of the target pixel; and determining a target object corresponding to the target pixel and association information of the target object according to the classification to which the target pixel belongs.
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公开(公告)号：US20220351398A1
公开(公告)日：2022-11-03
申请号：US17813870
申请日：2022-07-20
Inventor： Zhikang Zou , Xiaoqing Ye , Hao Sun
Abstract: A depth detection method, a method for training a depth estimation branch network, an electronic device, and a storage medium are provided, which relate to the field of artificial intelligence, particularly to the technical fields of computer vision and deep learning, and may be applied to intelligent robot and automatic driving scenarios. The specific implementation includes: extracting a high-level semantic feature in an image to be detected, wherein the high-level semantic feature is used to represent a target object in the image to be detected; inputting the high-level semantic feature into a pre-trained depth estimation branch network, to obtain distribution probabilities of the target object in respective sub-intervals of a depth prediction interval; and determining a depth value of the target object according to the distribution probabilities of the target object in the respective sub-intervals and depth values represented by the respective sub-intervals.
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公开(公告)号：US11915439B2
公开(公告)日：2024-02-27
申请号：US17353634
申请日：2021-06-21
Inventor： Xiaoqing Ye , Hao Sun
CPC classification number: G06T7/55 , G06N3/08 , G06T7/174 , G06T7/70 , G06T11/60 , G06T2207/20081 , G06T2207/20084
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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Patent Agency Ranking
公开(公告)号：US11893708B2
公开(公告)日：2024-02-06
申请号：US17505889
申请日：2021-10-20
Inventor： Jian Wang , Xiang Long , Hao Sun , Zhiyong Jin , Errui Ding
IPC: G06K9/00 , G06T3/40 , G06F18/213 , G06F18/25 , G06N3/045
CPC classification number: G06T3/4046 , G06F18/213 , G06F18/253 , G06N3/045
Abstract: Provided are an image processing method and apparatus, a device, and a storage medium, relating to the technical field of image processing, in particular to the artificial intelligence fields such as computer vision and deep learning. The specific implementation scheme is as follows: inputting a to-be-processed image into an encoding network to obtain a basic image feature, wherein the encoding network includes at least two cascaded overlapping encoding sub-networks which perform encoding and fusion processing on input data at at least two resolutions; and inputting the basic image feature into a decoding network to obtain a target image feature for pixel point classification, wherein the decoding network includes at least one cascaded overlapping decoding sub-network to perform decoding and fusion processing on input data at at least two resolutions respectively.
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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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公开(公告)号：US20230122373A1
公开(公告)日：2023-04-20
申请号：US18083272
申请日：2022-12-16
Inventor： Xiaoqing Ye , Hao Sun
IPC: G06T7/55
Abstract: A method for training a depth estimation model includes: obtaining sample images; generating sample depth images and sample residual maps corresponding to the sample images; determining sample photometric error information corresponding to the sample images based on the sample depth images; and obtaining a target depth estimation model by training an initial depth estimation model based on the sample images, the sample residual maps and the sample photometric error information.
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公开(公告)号：US20220392192A1
公开(公告)日：2022-12-08
申请号：US17890020
申请日：2022-08-17
Inventor： Zhigang Wang , Jian Wang , Hao Sun
IPC: G06V10/74 , G06V10/98 , G06V10/764 , G06V40/50 , G06V10/77
Abstract: A target re-recognition method, a target re-recognition device and an electronic device are provided, which relate to the field of artificial intelligence, in particularly to the field of computer vision and deep learning. The target re-recognition method includes obtaining a to-be-recognized image, and the to-be-recognized image including image content of a target object; recognizing first appearance presentation information corresponding to the target object, and the first appearance presentation information being configured to represent a presentation form of an appearance of the target object in the to-be-recognized image; obtaining from a data retrieval library a candidate retrieval image matching the first appearance presentation information; and performing target re-recognition on the to-be-recognized image based on the candidate retrieval image.
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