Target detection model training method based on active learning

Abstract

The invention provides a target detection model training method based on active learning, and the method comprises the steps: inputting a sample image marked with an expert label into a model training data set through an expert calibration input data set, and / or inputting a sample image without target object frame selection into the model training data set through an uncalibrated input data set; when a sample image quantity value of the model training data set is smaller than or equal to a data upper limit value, continuing to train the initial model, when the sample image quantity value is greater than the data upper limit value, selecting a screened image and then training the initial model, and selecting an input difficult sample data set to be calibrated without an expert label from the screened image; performing target object frame selection screening and expert label marking on the screened image, inputting the screened image into an expert calibration input data set, repeating the scheme, and generating a target detection model. The problem of low model training efficiency caused by data volume accumulation of the training data set in order to improve the generalization ability of the target detection model in the prior art is solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for training a target detection model based on active learning. Background technique [0002] Target detection is an image understanding algorithm based on target geometric and statistical features. Target detection combines the positioning and recognition of target objects. For example, based on computer vision algorithms, different types of target objects in the image are detected, that is, by The rectangular frame marks the location of the target and identifies the category of the target object. [0003] In order to make the target detection model suitable for different environmental scenarios and improve the generalization ability of the target detection model, in the process of training the target detection model, the model parameters are usually continuously optimized and adjusted to achieve the purpose of model refinement; this requires Periodically inpu...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a target detection model training method based on active learning to solve the problem of the accumulation of data in the training data set in order to improve the generalization ability of the model during the training process of the target detection model in the prior art. The problem of low training efficiency

Images