Image annotation information processing method and device, server and system

Abstract

An embodiment of the invention discloses an image annotation information processing method and device, a server and a system. Supervision and judgment processing logic of a plurality of nodes with different processing results can be provided. When image annotation information goes wrong, the results can be automatically returned, so that operators can perform review, modification and the like. Theprofessional ability of the operators can be improved by continuous auditing feedback interaction, image annotation efficiency is gradually improved, and training set picture annotation accuracy is greatly improved. Annotation quality can be effectively ensured, timely and effective information feedback is provided in workflow, and sample image annotation information operation efficiency is improved.

Description

technical field [0001] The embodiments of this specification belong to the technical field of computer data processing, and in particular, relate to a method, device, server and system for processing annotation information in images. Background technique [0002] The methods of detecting objects in images in existing applications mainly rely on the target detection / recognition models obtained through training, such as the parts recognition model in the vehicle damage assessment business, and the model for determining purchased items by taking pictures, etc. These target detection / recognition models usually need to rely on a large number of marked sample images for training, so the accuracy of the label information in the sample images has a great impact on the model output. [0003] When processing the annotation information of the sample image in the early stage, the sample image often contains multiple target subjects. The multi-target labeling task is more complicated th...

AI Technical Summary

Problems solved by technology

However, when the number of sample images to be processed is large and the target subjects in most images are relatively dense (at least two), manual labeling processing requires both professionalism and It is also easy to confuse, which often leads to low accuracy of the output sample image annotation information, and it is difficult to guarantee the quality of annotation, which in turn leads to poor image recognition algorithms and low target prediction accuracy in the image.

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