RGB-D image salient target detection method

Abstract

The invention discloses an RGB-D image salient target detection method. The method comprises the following steps: extracting RGB and Depth image features, simultaneously implementing fusion to form RGBD fusion features, and dividing the RGBD fusion features into high-level RGBD fusion features and low-level RGBD fusion features; enhancing the high-level RGBD fusion features by using three Transformers to form high-level RGBD enhanced features; performing three-stream decoding on the high-level RGBD enhanced features, and forming RGBD refined features by combining the high-level RGBD enhanced features with the low-level RGBD fusion features; and fusing the RGBD refined features to form a saliency map. According to the detection method, the high-level features are enhanced by utilizing Transformer, position information of a salient object is accurately obtained, the low-level features are fused by utilizing three-stream fusion, and contour details of the salient object are refined; and through combination of a convolutional neural network and Transformer, and global and local optimization, the saliency map with high precision is generated.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a salient object detection method of an RGB-D image using three Transformers to enhance high-level features. Background technique [0002] RGB-D image is composed of RGB color image and Depth depth image, expressing the appearance characteristics and three-dimensional information of a certain scene. At present, there are two multi-modal fusion methods in RGB-D image salient target detection methods, one is dual-stream fusion, which treats color and depth images equally, and the other is depth-guided fusion, which focuses on color images and supplements depth images. . In view of the existence of some poor-quality depth images, the use of dual-stream fusion may enlarge the depth error and adversely affect the final salient object detection results. Therefore, depth-guided fusion is a better fusion method. [0003] Image salient object detection is a dense prediction task at the p...

AI Technical Summary

Problems solved by technology

In view of the existence of some poor-quality depth images, the use of dual-stream fusion may enlarge the depth error and adversely affect the final salient target detection results.

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