Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Innopower Superconductor Cable Co., Ltd.

Ying Xin

Bin Zhang

Yuan Feng

Pengcheng Yuan

Xiaodi Wang

Shumin Han

Shufei Lin

Errui Ding

Shilei Wen

Hongwu Zhang

Xiang Long

Jingwei Liu

Yingbo Xu

Yan Peng

Jianfeng Zhu

Zhuang Jia

Weizhi Gong

Xiaoye Niu

Zhengjian Cao

Mingyuan Mao

Honghui Zheng

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.

Method and apparatus for evaluating image relative definition, device and medium

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.

Method for detecting surface defect, method for training model, apparatus, device, and media

An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

Object area measurement method, electronic device and storage medium

The present application discloses a method and an apparatus for detecting wearing of a safety helmet, a device and a storage medium. The method for detecting wearing of a safety helmet includes: acquiring a first image collected by a camera device, where the first image includes at least one human body image; determining the at least one human body image and at least one head image in the first image; determining a human body image corresponding to each head image in the at least one human body image according to an area where the at least one human body image is located and an area where the at least one head image is located; and processing the human body image corresponding to the at least one head image according to a type of the at least one head image.

Method and apparatus for detecting wearing of safety helmet, device and storage medium

A core-saturated superconductive fault current limiter and a control method of the fault current limiter. The fault current limiter includes a superconductive magnet (), a core (), an AC winding (), a cryostat system, a monitor system () and a DC control system (). The output of the DC control system () is connected to the two terminals of the superconductive magnet (). The DC control system () is also connected to the monitor system (). The core () has an unequal section core structure. The control method includes: controlling the current which is flowing through the superconductive magnet () for limiting the fault current in the power net () in the case of a short circuit fault event.

Core-saturated superconductive fault current limiter and control method of the fault current limiter

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Beijing, China

Ying Xin