Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Yanwu Xu

Lei Wang

Yehui Yang

Binghong Wu

Yan Huang

Qian Li

Fangxin Shang

DaLU Yang

Technical solutions relate to the field of artificial intelligence such as deep learning, computer vision and intelligent imaging. A method may includes during training of a one-stage object detecting model, acquiring values of a loss function corresponding to feature maps at different scales respectively in the case that classification loss calculation is required, and the loss function is a focal loss function; and determining a final value of the loss function according to the acquired values of the loss function, and training the one-stage object detecting model according to the final value of the loss function.

One-stage object detecting model training method, electrical device and storage medium

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.

Method, apparatus, and device for fusing features applied to small target detection, and storage medium

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.

Target detection

The present application discloses an image processing method, an apparatus, an electronic device and a storage medium. A specific implementation is: acquiring an image to be processed; acquiring a grading array according to the image to be processed and a grading network model, where the grading network model is a model pre-trained according to mixed samples, the number of elements contained in the grading array is C&#x2212;1, C is the number of lesion grades, C lesion grades include one lesion grade without lesion and C&#x2212;1 lesion grades with lesion, and a kth element in the grading array is a probability of a lesion grade corresponding to the image to be processed being greater than or equal to a kth lesion grade, where 1&#x2264;k&#x2264;C&#x2212;1, and k is an integer; determining the lesion grade corresponding to the image to be processed according to the grading array.

Image processing method, apparatus, electronic device and storage medium

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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Beijing, China

Yanwu Xu