Beijing Sensetime Technology Development Co., Ltd.

Honda Motor Co., Ltd.

Sensetime Group Limited

Beijing Huayun Shinetek Technologies Co., Ltd.

Junjie Yan

Xiaogang Wang

Xingyu Zeng

Quanquan Li

Buyu Li

Yingjie Cai

Shinan Liu

Liang Liu

Yuji Yasui

Atsushi Kawamura

Yuji Kaneda

Tokitomo Ariyoshi

Shuai Yi

Chenglin Liu

Zhao Zhong

Yuhi Goto

Chongdao Wang

Zhiwei Fang

Luming Tang

Qixing Li

Fengwei Yu

A method for a platform-based scheduling of a job flow is provided, which relates to the technology field of satellites. Specifically, the method includes: acquiring satellite remote sensing data according to transit time of a satellite and triggering a processing flow of the satellite remote sensing data; determining an execution order of the processing flow of the satellite remote sensing data according to a constraint relationship; and allocating processing resources to the processing flow in a hierarchical scheduling manner according to the execution order, and executing the processing flow with the processing resources. Due to a reasonable determination of the execution order of the processing flow of the satellite remote sensing data and a scheduling of the processing resources in the hierarchical scheduling manner, a reasonable allocation of the processing resources is realized. Thus, A timeliness of the processing of the satellite remote sensing data may be improved.

Method for platform-based scheduling of job flow

Embodiments of the present application disclose methods and apparatuses for determining a bounding box of a target object, media, and devices. The method includes: obtaining attribute information of each of a plurality of key points of a target object; and determining a bounding box position of the target object according to the attribute information of each of the plurality of key points of the target object and to a preset neural network. The implementations of the present application can improve the efficiency and accuracy of determining a bounding box of a target object.

Methods and apparatuses for determining bounding box of target object, media, and devices

Embodiments of the present application disclose a method and apparatus for generating a neural network structure, an electronic device, and a storage medium. The method comprises: sampling a neural network structure to generate a network block, the network block comprising at least one network layer; constructing a sampling neural network based on the network block; training the sampling neural network based on sample data, and obtaining an accuracy corresponding to the sampling neural network; and in response to that the accuracy does not meet a preset condition, regenerating a new network block according to the accuracy until a sampling neural network constructed by the new network block meets the preset condition, and using the sampling neural network meeting the preset condition as a target neural network.

Method and apparatus for generating target neural network structure, electronic device, and storage medium

The method for vehicle appearance feature recognition includes: multiple region segmentation results of a target vehicle are obtained from an image to be recognized; global feature data and multiple pieces of region feature data are extracted from the image to be recognized based on the multiple region segmentation results; and the global feature data and the multiple pieces of region feature data are fused to obtain appearance feature data of the target vehicle.

Methods and apparatuses for vehicle appearance feature recognition, methods and apparatuses for vehicle retrieval, storage medium, and electronic devices

A method for object detection includes: obtaining a plurality of to-be-determined targets in a to-be-detected image; determining confidences of the plurality of to-be-determined targets separately belonging to at least one category, determining categories of the plurality of to-be-determined targets according to the confidences, and determining position offset values corresponding to the respective categories of the plurality of to-be-determined targets; using the position offset values corresponding to the respective categories of the plurality of to-be-determined targets as position offset values of the plurality of to-be-determined targets; and determining position information and a category of at least one to-be-determined target in the to-be-detected image according to the categories of the plurality of to-be-determined targets, the position offset values of the plurality of to-be-determined targets, and the confidences of the plurality of to-be-determined targets belonging to the categories thereof.

Methods and apparatuses for object detection, and devices

A target object recognition method includes: performing target object detection on an object of an image to be detected to obtain target object prediction information of the object, where the target object prediction information is confidence information that the detected object is the target object; performing key point detection on the object of the image to be detected to obtain key point prediction information of the object, where the key point prediction information is confidence information that a key point of the detected object is a key point of the target object; fusing the target object prediction information with the key point prediction information to obtain comprehensive prediction information of the object; and recognizing the target object according to the comprehensive prediction information.

Target object recognition method and apparatus, storage medium, and electronic device

A 3D object detection method includes: obtaining two-dimensional (2D) coordinates of at least one predetermined key point of a target object in an image to be processed; constructing a pseudo 3D detection body of the target object in a 2D space according to the 2D coordinates of the at least one predetermined key point; obtaining depth information of a plurality of vertices of the pseudo 3D detection body; and determining a 3D detection body of the target object in a 3D space according to the depth information of the plurality of vertices of the pseudo 3D detection body.

Three-dimensional object detection method and device, method and device for controlling smart driving, medium and apparatus

Disclosed in embodiments of the present disclosure are an object three-dimensional detection method and apparatus, an intelligent driving control method and apparatus, a medium, and a device. The object three-dimensional detection method comprises: obtaining two-dimensional coordinates of a key point of a target object in an image to be processed; constructing a pseudo three-dimensional detection body of the target object according to the two-dimensional coordinates of the key point; obtaining depth information of the key point; and determining a three-dimensional detection body of the target object according to the depth information of the key point and the pseudo three-dimensional detection body.

Object three-dimensional detection method and apparatus, intelligent driving control method and apparatus, medium and device

A vehicle lamp detection method includes: obtaining an image block including an image of a vehicle; and performing vehicle lamp detection on the image block by means of a deep neural network, to obtain a vehicle lamp detection result.

Vehicle lamp detection methods and apparatuses, methods and apparatuses for implementing intelligent driving, media and devices

Methods, apparatuses and electronic devices for identifying an object category include: determining M key point neighborhood regions from corresponding object candidate boxes according to position information of M key points in a plurality of object candidate boxes of an image to be detected, where M is less than or equal to the total number of key points of N preset object categories, and M and N are positive integers; and determining category information of at least one object in the image to be detected using a convolutional neural network model used for identifying an object category in the image according to the M key point neighborhood regions.

Methods and apparatuses for identifying object category, and electronic devices

A method and an apparatus for detecting an object keypoint, an electronic device, a computer readable storage medium, and a computer program include: obtaining a respective feature map of at least one local regional proposal box of an image to be detected, the at least one local regional proposal box corresponding to at least one target object; and separately performing target object keypoint detection on a corresponding local regional proposal box of the image to be detected according to the feature map of the at least one local regional proposal box.

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Beijing, China

Junjie Yan