Image labeling method and device

Abstract

The invention provides an image annotation method and device, and the method comprises the steps: receiving an image display instruction, and obtaining a to-be-annotated image according to the image display instruction; displaying the image to be labeled in a canvas in a target browser; and monitoring a mouse click event in the canvas in real time, if the mouse click event is monitored in real time, obtaining a to-be-annotated position in the to-be-annotated image according to the mouse click event monitored in real time, and annotating the to-be-annotated position by applying a preset annotation element to obtain an annotated image. The image annotation quality and efficiency can be improved, and then the accuracy and efficiency of transaction certificate image classification or intelligent driving image detection achieved by applying the image annotation result can be improved.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image labeling method and device. Background technique [0002] With the development of science and technology, the Internet has fully entered the era of artificial intelligence. The current rapid development of artificial intelligence relies on the combination of big data and machine learning. Ability to predict complex patterns. For example, in order for an unmanned vehicle to acquire the ability to recognize pedestrians, vehicles and obstacles on the road, it needs to go through deep learning on tens of millions or even hundreds of millions of training data sets. Deep learning requires a large amount of labeled data to support, so a massive training data set can be provided through data collection and labeling. At present, most of the data labeling work is actually image data labeling, such as image recognition, face recognition, user behavior analysis,...

AI Technical Summary

Problems solved by technology

[0004] The image data annotation method based on the crowdsourcing model has the problem of low image annotation quality; the online data system has the problems of high system usage cost and low data security; applying the existing open source artificial intelligence image annotation tools on the Internet, Its installation files are generally client applications, which do not support multi-person collaboration and have low efficiency

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