Picture labeling method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a picture marking method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: identifying a to-be-processed picture, and determining detection frame information corresponding to each single product in the to-be-processed picture; sorting the detection frames corresponding to the detection frame information, and determining single item identifiers corresponding to the sorted detection frames based on preset single item identifier arrangement information; and according to the information of each detection frame,cutting out each single-item picture carrying a single-item identifier from the to-be-processed picture. According to the method and the device, each single product in the to-be-processed picture isframed through the identified information of each detection frame; According to the technical scheme, the pictures to be processed are cut on the basis of the detection frames, a large number of single-item pictures can be obtained rapidly, the collection efficiency is greatly improved, the single-item identifiers of the single-item pictures can be directly obtained on the basis of the single-itemidentifiers corresponding to the detection frames, a large number of labeling tasks can be completed rapidly, the labor cost is saved, and the labeling efficiency is improved.

Description

technical field [0001] The present application relates to the technical fields of machine vision and artificial intelligence. Specifically, the present application relates to a method, device, electronic equipment and computer-readable storage medium for marking pictures. Background technique [0002] Artificial intelligence technology is gradually affecting people's lives. Many companies are using the power of artificial intelligence to quietly change our consumption patterns and bring disruptive changes to traditional retail formats. Taking the unmanned supermarket as an example, as an important application scenario in the retail industry under the integration of the Internet of Things and the Internet, especially with the increasing demand for unmanned settlement and fast cashier in the new retail scenario, the unmanned supermarket based on machine vision The scene needs to train a deep neural network (such as CNN, Convolutional Neural Network, Convolutional Neural Networ...

AI Technical Summary

Problems solved by technology

[0004] However, the manual collection and labeling method not only has high labor costs, but also has low efficiency when relying entirely on manual collection and labeling. Moreover, human errors are inevitable, and inadvertent errors will cause the quality of labeling information to decrease, which will affect the accuracy of the deep neural network model. In the end, it will cause inconvenience to users

Images