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Patent Agency Ranking
公开(公告)号：US20220147822A1
公开(公告)日：2022-05-12
申请号：US17459066
申请日：2021-08-27
Inventor： Ying XIN , Yuan FENG , Guanzhong WANG , Pengcheng YUAN , Bin ZHANG , Xiaodi WANG , Xiang LONG , Yan PENG , Honghui ZHENG , Shumin HAN
IPC: G06N3/08 , G06N3/04 , G06V10/82 , G06V10/766 , G06V10/77
Abstract: Provided are a training method and apparatus for a target detection model, a device and a storage medium. The training method is described below. A feature map of a sample image is processed through a classification network of an initial model and a heat map and a classification prediction result of the feature map are obtained, a classification loss value is determined according to the classification prediction result and classification supervision data of the sample image, and a category probability of pixels in the feature map is determined according to the heat map of the feature map and a probability distribution map of the feature map is obtained; the feature map is processed through a regression network of the initial model and a regression prediction result is obtained, and a regression loss value is determined.
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2.发明申请公开(公告)号：US20210390682A1
公开(公告)日：2021-12-16
申请号：US17116597
申请日：2020-12-09
Inventor： Shufei LIN , Jianfeng ZHU , Pengcheng YUAN , Bin ZHANG , Shumin HAN , Yingbo XU , Yuan FENG , Ying XIN , Xiaodi WANG , Jingwei LIU , Shilei WEN , Hongwu ZHANG , Errui DING
Abstract: A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.
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公开(公告)号：US20210312240A1
公开(公告)日：2021-10-07
申请号：US17348285
申请日：2021-06-15
Inventor： Xiaodi WANG , Shumin HAN , Yuan FENG , Ying XIN , Bin ZHANG , Shufei LIN , Pengcheng YUAN , Xiang LONG , Yan PENG , Honghui ZHENG
Abstract: A header model for instance segmentation includes a target box branch having a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box. The header model also includes a mask branch configured to process an inputted second feature map to obtain mask information, wherein the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map.
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公开(公告)号：US20210350173A1
公开(公告)日：2021-11-11
申请号：US17379428
申请日：2021-07-19
Inventor： Xiang LONG , Yan PENG , Shufei LIN , Ying XIN , Bin ZHANG , Pengcheng YUAN , Xiaodi WANG , Yuan FENG , Shumin HAN
Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.
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5.发明申请公开(公告)号：US20220020175A1
公开(公告)日：2022-01-20
申请号：US17489991
申请日：2021-09-30
Inventor： Xiaodi WANG , Shumin HAN , Yuan FENG , Ying XIN , Bin ZHANG , Xiang LONG , Honghui ZHENG , Yan PENG , Zhuang JIA
Abstract: An object detection model training method, object detection method and related apparatus, relate to the field of artificial intelligence technologies such as computer vision, deep learning. An implementation includes: obtaining training sample data including a first remote sensing image and position annotation information of an anchor box of a subject to be detected in the first remote sensing image, where the position annotation information includes angle information of the anchor box relative to a preset direction; obtaining an object feature map of the first remote sensing image based on an object detection model, performing object detection on the subject to be detected based on the object feature map to obtain an object bounding box, and determining loss information between the anchor box and the object bounding box based on the angle information; updating a parameter of the object detection model based on the loss information.
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