Image labeling method and device and storage medium

Abstract

The invention provides an image labeling method and device and a storage medium, and the method comprises the steps: receiving a to-be-detected image input by a user, the to-be-detected image comprising a target object located in a target scene; and adopting an object detection model to mark a target object in the to-be-detected image, the object detection model being obtained by training according to the to-be-trained image set, the to-be-trained image set comprising a plurality of to-be-trained images synthesized by an image comprising the same scene type as the target scene and an image comprising the target object. According to the invention, the image in the to-be-trained image set used by the object detection model adopted by the image labeling is synthesized by the image containing the target object and the scene of which the scene type is the same as that of the target scene; the image does not need to be labeled in advance, the efficiency of obtaining the object detection model is improved, and then the image labeling efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of deep learning, and in particular to an image labeling method, device and storage medium. Background technique [0002] With the development of deep learning technology, object detection models based on deep learning technology are used in more and more scenes to solve practical problems. For example, scenarios such as shelf inspection and unmanned settlement for commodities, drone inspection scenarios for crops, and assembly line inspection scenarios for industrial standard parts. Among them, the object detection model can identify and mark the target object in the image. For example, in an unmanned settlement scenario, the object detection model can identify and label the commodities in the acquired images, and then settle the commodities in the images to achieve the purpose of unmanned settlement. [0003] In the prior art, it is necessary to collect and label a large number of images as tra...

AI Technical Summary

Problems solved by technology

The acquisition efficiency of training data in the prior art is low, which in turn leads to low efficiency of image labeling

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