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Patent Agency Ranking
公开(公告)号：US20210224581A1
公开(公告)日：2021-07-22
申请号：US17213896
申请日：2021-03-26
Inventor： Binghong Wu , Yehui Yang , Yanwu Xu , Lei Wang
Abstract: Embodiments of the present disclosure disclose a method, apparatus, and device for fusing features applied to small target detection, and a storage medium, relate to the field of computer vision technology. A particular embodiment of the method for fusing features applied to small target detection comprises: acquiring feature maps output by convolutional layers in a Backbone network; performing convolution on the feature maps to obtain input feature maps of feature layers, the feature layers representing resolutions of the input feature maps; and fusing, based on densely connection feature pyramid network features, the input feature maps of each feature layer to obtain output feature maps of the feature layer. Since no additional convolutional layer is introduced for feature fusion, the detection performance for small targets may be enhanced without additional parameters, and the detection ability for small targets may be improved with computing resource constraints.
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公开(公告)号：US20200320686A1
公开(公告)日：2020-10-08
申请号：US16701025
申请日：2019-12-02
Inventor： Yehui Yang , Yanwu Xu , Lei Wang , Yan Huang
Abstract: Embodiments of the present disclosure provide a method and apparatus for processing a fundus image. The method may include: acquiring a target fundus image; dividing the target fundus image into at least two first image blocks; inputting a first image block into a pre-trained deep learning model, to obtain a first output value; and determining, based on the first output value and a threshold, whether the first image block is the fundus image block containing a predetermined type of image region.
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公开(公告)号：US11735315B2
公开(公告)日：2023-08-22
申请号：US17213896
申请日：2021-03-26
Inventor： Binghong Wu , Yehui Yang , Yanwu Xu , Lei Wang
CPC classification number: G16H30/40 , G06F18/251 , G06F18/253 , G06N3/045 , G06V10/764 , G06V10/806 , G06V10/82 , G16H50/20
Abstract: Embodiments of the present disclosure disclose a method, apparatus, and device for fusing features applied to small target detection, and a storage medium, relate to the field of computer vision technology. A particular embodiment of the method for fusing features applied to small target detection comprises: acquiring feature maps output by convolutional layers in a Backbone network; performing convolution on the feature maps to obtain input feature maps of feature layers, the feature layers representing resolutions of the input feature maps; and fusing, based on densely connection feature pyramid network features, the input feature maps of each feature layer to obtain output feature maps of the feature layer. Since no additional convolutional layer is introduced for feature fusion, the detection performance for small targets may be enhanced without additional parameters, and the detection ability for small targets may be improved with computing resource constraints.
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公开(公告)号：US11232560B2
公开(公告)日：2022-01-25
申请号：US16701025
申请日：2019-12-02
Inventor： Yehui Yang , Yanwu Xu , Lei Wang , Yan Huang
Abstract: Embodiments of the present disclosure provide a method and apparatus for processing a fundus image. The method may include: acquiring a target fundus image; dividing the target fundus image into at least two first image blocks; inputting a first image block into a pre-trained deep learning model, to obtain a first output value; and determining, based on the first output value and a threshold, whether the first image block is the fundus image block containing a predetermined type of image region.
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公开(公告)号：US11436447B2
公开(公告)日：2022-09-06
申请号：US17039554
申请日：2020-09-30
Inventor： Yehui Yang , Lei Wang , Yanwu Xu
Abstract: A target detection method a is provided, which relates to the fields of deep learning, computer vision, and artificial intelligence. The method comprises: classifying, by using a first classification model, a plurality of image patches comprised in an input image, to obtain one or more candidate image patches, in the plurality of image patches, that are preliminarily classified as comprising a target; extracting a corresponding salience area for each candidate image patch; constructing a corresponding target feature vector for each candidate image patch based on the corresponding salience area for each candidate image patch; and classifying, by using a second classification model, the target feature vector to determine whether each candidate image patch comprises the target.
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