Automatic image labeling system and method for new retail

Abstract

The invention relates to an automatic image labeling system and method for new retail. The system comprises a test unit configured to a to-be-annotated picture and used for automatically annotating the picture corresponding to the to-be-annotated picture, a cascade network test unit used for acquiring position information of a target and the label of the target in the to-be-annotated picture, a test unit used for checking whether a labeling result is Groud Truth or not, and a test unit used for obtaining the labeling result. An image segmentation model for a to-be-labeled target is trained, the to-be-annotated picture is shot, and a paper sheet written with numbers on a target of the to-be-labeled picture for automatic labeling is placed, position information of a target in the picture isobtained through an image segmentation network, whether errors are generated or not, digital information on the target is obtained through a deep learning network, the label of the target is confirmedthrough a corresponding relationship between numbers and labels after checking errors do not exist, and a. Csv format file is generated according to a filename of the to-be-annotated picture, position information of each target in the picture and the labels to finish automatic labeling.

Description

technical field [0001] The invention relates to the technical field of image tagging, in particular to an image automatic tagging system and method for new retail. Background technique [0002] Image annotation is a technique to mark the position and label of the target in the image. The popularization of artificial intelligence technology makes data labeling appear in front of everyone as a new industry. In the field of machine vision, image labeling has become a complex and cumbersome task with high demand. Improving the efficiency of image labeling has become a hot spot. [0003] The traditional annotation method is to mark the position of the target object in the original image and generate a corresponding annotation file for each picture to represent the position information of the target and the label of the target. The traditional labeling method has high reliability, but there are problems of low efficiency and serious waste of human resources. The existing automat...

AI Technical Summary

Problems solved by technology

The traditional labeling method has high reliability, but there are problems of low efficiency and serious waste of human resources

The existing automatic labeling technology uses a trained target recognition network for a specific scene to label. This labeling method has a high degree of automation, but it cannot guarantee the correctness of the labeling results. The labels are highly targeted and have poor scalability.

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