Image data generation method and device

Abstract

One or more embodiments of the invention provide an image data generation method and device. The method comprises the steps of obtaining a simulation object model of a target object and a simulation environment model of a target scene; constructing a simulation scene of the target scene based on the simulation object model and the simulation environment model; generating a rendered image based onthe simulation scene, and determining annotation information of the rendered image, the annotation information being used for representing distribution information of a simulation object model contained in the simulation scene in the rendered image. A simulation scene of a target scene is automatically constructed; rendering the simulation scene by using a three-dimensional rendering technology toobtain a plurality of target annotation images; therefore, a large number of actually shot images do not need to be shot on site, the actually shot images do not need to be labeled manually, the synthesized image with high image reality sense and high labeling accuracy can be generated quickly, and a large number of available sample data with labeling information are provided for model training.

Description

technical field [0001] One or more of this specification relates to the technical field of artificial intelligence, and in particular to a method and device for generating image data. Background technique [0002] At present, with the rapid development of machine learning and artificial intelligence, machine learning technology has been widely applied to various fields, solving problems that most traditional algorithms cannot solve, for example, in image recognition, speech recognition, natural language processing and other fields , machine learning techniques have played a pivotal role. [0003] However, for machine learning (especially deep learning), due to its inexplicability, the accuracy and recall of machine learning models (especially deep learning models) depend on the quantity and quality of training samples. Therefore, it is necessary to Only by obtaining a large amount of sample data with labeled information can a neural network recognition model with high accur...

AI Technical Summary

Problems solved by technology

[0004] At present, the sample data with annotation information is mainly obtained by collecting a large number of live images, and then manually annotating the information on the live images. On the one hand, it is difficult to obtain a large number of on-site images. There are problems of low labeling efficiency, high labor cost, large influence of human factors, and low accuracy, making it difficult to generate a large amount of labeling data required for training models in a short period of time

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