Target position labeling method, device and equipment

Abstract

The embodiment of the invention provides a target position labeling method, device and equipment, and the method comprises the steps of carrying out the feature extraction of a first image and a second image, and obtaining a first feature matrix of the first image and a second feature matrix of the second image, wherein the type of the first image belongs to violation, the type of the second imagebelongs to non-violation, and the first image and the second image are obtained by shooting the same scene; obtaining first position information of each first feature element in the first feature matrix in the first feature matrix; obtaining second position information of each second feature element in the second feature matrix in the second feature matrix; and determining an intersection of theacquired first position information and the acquired second position information, and taking the position information in the intersection as difference feature position information; based on the difference feature position information, determining the position information corresponding to the difference feature position information in the first image as the target position information of the firstimage, i.e., the target position automatically marked by the first image.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a target position labeling method, device and equipment. Background technique [0002] In the field of machine vision technology, the target detection technology used to find out the location of the item from the picture and determine the type of the item is widely used. For example, to use target detection to identify road-occupied operations, you can input street images into a pre-trained target detection network model to obtain the location of business stalls in the street image, and determine whether the business stalls are operating illegally, for example, road-occupied operations , or unoccupied business. [0003] The above-mentioned pre-trained target detection network model is a neural network model obtained by using a large number of samples to compare images in advance for training. Among them, the sample comparison image refers to a non-violation sample image ...

AI Technical Summary

Problems solved by technology

However, for the training of the target detection network, it is often necessary to use a large number of samples to compare images, resulting in a large workload of manual labeling, and human subjective factors in manual labeling may also lead to labeling errors or missing, which in turn affects the target detection obtained in subsequent training. Detection Accuracy of Network Models

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