Item labeling method and device, electronic equipment and storage medium

Abstract

The embodiment of the present invention relates to the technical field of machine vision, and discloses an item labeling method and device, electronic equipment, and a storage medium. The method includes: generating a two-category item labeling model for labeling targets and non-targets; inputting a number of pictures to be marked; wherein, the targets in the pictures to be marked are marked with categories, and each target containing the target The target object in the marked image is one type; the image to be marked is automatically marked through the binary classification item labeling model to obtain the position information of the target object, and the marked category is used as the target object category output by the binary classification item labeling model. The embodiment of the present invention can realize item labeling more quickly and at low cost.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to an item labeling method and device, electronic equipment and a storage medium. Background technique [0002] With the development of artificial intelligence and machine learning technology, unattended stores have begun to appear. The operation of unattended stores needs to rely on object detection technology. Target detection technology is a branch of the field of machine vision, which is mainly used to identify the position and category of the target object on the picture. At present, common methods of object detection include: traditional object detection methods and object detection frameworks based on deep learning. Existing object detection frameworks are mainly supervised learning. The supervised learning method is to generate a function to map the input to the appropriate output through the corresponding relationship between a part of the input data and the outpu...

AI Technical Summary

Problems solved by technology

Since there are thousands of items in real life, using the above labeling method, it is necessary to relabel and update the model for items that have not appeared in the model, which consumes a lot of manpower and material resources

At the same time, when there are too many types of items, the accuracy of the above labeling methods will drop sharply due to the similarity between items.

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