Multi-feature cyclic convolution saliency target detection method based on attention mechanism

Abstract

The invention discloses a multi-feature cyclic convolution significance target detection method based on an attention mechanism. The method comprises the following steps: ; the method comprises the following steps of: 1, analyzing common characteristics of a salient target in a natural image, including spatial distribution and contrast characteristics, using an improved U-Net full convolutional neural network, performing pixel-by-pixel prediction by adopting an encoder-decoder structure, and performing multi-level and multi-scale characteristic fusion between an encoder and a decoder by adopting a cross-layer connection mode; secondly, a large number of clutters can be introduced to interfere with the generation of a final prediction graph by carrying out concentage fusion on coding end features and decoding end features, so that an attention module is introduced to calibrate full-pixel weights from two angles between channels and between pixels, the task-related pixel weights are enhanced, and the background and noise influence is weakened; and 3, a multi-feature cyclic convolution module is used as a post-processing means, the spatial resolution capability is enhanced through iteration, the edge of an image region is further refined and segmented, and a finer significant target mask is obtained.

Description

technical field [0001] The invention belongs to the field of automatic target recognition, and in particular relates to a research on a multi-feature circular convolution salient target detection method based on an attention mechanism. Background technique [0002] Salient object detection is a basic research hotspot in the field of machine vision. Using the salient object detection method to calculate the saliency of image pixels can highlight the pixels in the foreground area of ​​the image, suppress the pixels in the background area, and achieve the purpose of data dimensionality reduction and background interference reduction. The generated saliency map will help to rationally allocate limited computing resources and provide prior information for subsequent complex vision tasks. Salient object detection has a wide range of applications in image retrieval, image / video compression, image quality assessment, virtual vision and other fields. [0003] Traditional salient obj...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: the existing salient target detection method has problems such as insufficient feature utilization benefit, false background detection and blurred target outline. Therefore, a multi-feature circular convolution based on attention mechanism is provided. Sexual Target Detection Methods

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