Target candidate region extraction method based on image background mask

Abstract

The invention relates to a target candidate region extraction method based on an image background mask, comprising the following steps: (1) constructing an image background mask data set; (2) buildinga GAN model, and adding a background mask for an image by adopting a GAN training way; (3) defining a loss function, namely enabling a generated picture and a trained target picture to be similar tothe utmost extent when a detail part of high-frequency structural information in the image is processed, and defining the loss function to be a combination of a GAN target function and one-norm distance loss of a composite image; and (4) carrying out model training.

Description

technical field [0001] The invention belongs to the field of target detection and deep learning, and relates to a method for extracting target candidate regions in natural images by applying a generative confrontation network model based on the idea of ​​image masking. Background technique [0002] The extraction of the target candidate area is to find out the area where the target object may exist in the image, which belongs to the category of target detection. Traditional object detection methods usually use a sliding window method. This region selection strategy that traverses the entire image is not targeted, has high time complexity, and has redundant windows. On the basis of region nomination, by using the texture, edge, color and other information in the image, the possible location of the target object in the image can be found in advance. A commonly used region nomination algorithm is the selective search algorithm. [0003] Inspired by the structure of the visual...

AI Technical Summary

Problems solved by technology

Traditional object detection methods usually use a sliding window method. This region selection strateg

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