Salient target detection method based on super-large receptive field feature optimization

A feature optimization and target detection technology, applied in the field of image processing, can solve problems such as poor operation effect, achieve good results, optimize edge information, and enhance the effect of feature quality

Inactive Publication Date: 2021-08-27
ANHUI UNIV OF SCI & TECH
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Problems solved by technology

However, the effect of further extracting semantic features by stacking convolutional layers, larger c

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  • Salient target detection method based on super-large receptive field feature optimization
  • Salient target detection method based on super-large receptive field feature optimization
  • Salient target detection method based on super-large receptive field feature optimization

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Embodiment Construction

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the examples of the present invention. In addition, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary skilled persons in this research direction without any creative effort fall within the protection scope of the present invention.

[0039] The flowchart framework of the present invention is as figure 1 As shown, the present invention is based on a salient target detection method with a super large receptive field, and its specific operation is described as follows:

[0040] 1. A salient target detection method based on super large receptive field feature optimization, characterized in that the method comprises the following steps:

[0041] (1) Use ResNet-50 as th...

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Abstract

The invention belongs to the field of computer vision, and provides a salient target detection method based on super-large receptive field feature optimization, which comprises the following steps: 1) taking ResNet-50 as a trunk frame to extract multi-scale feature information from an RGB image and then encoding; 2) optimizing the multi-scale features by using a super-large receptive field feature mechanism to generate high-quality features; and 3) performing complementarity fusion on the optimized features by using an invisible relation feature fusion mechanism, and then generating a final saliency map. Compared with the prior art, according to the salient detection method based on super-large receptive field feature optimization, super-large receptive field optimization multi-scale features are utilized, invisible relation feature fusion is carried out, and the multi-scale features are optimized layer by layer to generate a high-performance saliency map.

Description

Technical field: [0001] The invention relates to the field of image processing, in particular to a salient target detection method based on super large receptive field feature optimization. Background technique: [0002] The statements in this section only relate to the background technology related to the present invention, and do not necessarily constitute the prior art. [0003] With the rapid popularization of Internet technology, multimedia technology and digital products, digital images have become an important carrier for people to obtain information from the outside world. And this means that efficiently and quickly processing a large amount of digital image data has become a crucial issue. It is very inefficient for a computer to process all the details in a digital image simultaneously. If the limited computer resources are allocated to the salient target areas in the image, the efficiency of computer processing digital images will be greatly improved. Therefore...

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Application Information

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IPC IPC(8): G06K9/32G06K9/34G06N3/04
CPCG06V10/25G06V10/267G06N3/045
Inventor 孙延光夏晨星段松松张海涛
Owner ANHUI UNIV OF SCI & TECH
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