Attention mechanism based salient object detection method

Abstract

An attention mechanism based salient object detection method relates to the fields of computer vision and digital image processing. The method comprises that a top down attention network is designed to purify different layers of convolution characteristics, a fusion network with characteristics of a residual error of second order term design is used to maintain characteristics of the residual error better, a saliency map in which any static color image serves as input and the size of output is consistent with that of the input image, and in the saliency map, white represents areas of salient objects, and black represents background areas. Thus, the saliency map of high resolution can be obtained, and small salient objects can be detected more effectively.

Description

technical field [0001] The invention relates to the fields of computer vision and digital image processing, in particular to a method for detecting a salient object based on an attention mechanism. Background technique [0002] Salient object detection is a popular research topic in the field of computer vision. Its purpose is to extract attractive objects or regions in an image and assign them a saliency value. As a preprocessing step, it can be applied to other high-level vision tasks such as weakly supervised semantic segmentation, object recognition, etc. Traditional salient object detection methods are based on manually designed features, such as contrast, background center difference, etc. These hand-designed low-level visual features are difficult to capture semantic information, and thus do not perform well in complex scenes. In recent years, thanks to the rapid development of deep learning, the performance of salient object detection has been greatly improved. How...

AI Technical Summary

Problems solved by technology

However, the existing salient object detection models are fine-tuned based on the image classification model. Due to the differences in the tasks of the two, the features learned by the image classification network cannot accurately locate the object boundary, and directly using it for salient object detection cannot obtain high resolution. The saliency map of the rate, especially at object boundaries

In addition, in order to be able to detect multi-scale salient objects, it is usually necessary to fuse convolutional features of different scales. However, the existing feature fusion methods simply add or merge, which tends to weaken the residual features, thus affecting the detection of small salient objects.

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