A RGBD Salient Object Detection Method Based on Siamese Network

Abstract

The invention discloses a twin network-based RGBD salient object detection method in the technical field of image processing and computer vision. The steps include: 1. Obtain the RGB image and the depth image of the picture to be detected; 2. Input the RGB image and the depth image into "" The twin network-decoder neural network outputs RGBD saliency detection results, and the "twin network-decoder" neural network is pre-trained jointly, including the twin network and decoder; the steps of S2 specifically include: RGB image and depth image input twin The network outputs the RGB and depth hierarchical features of the twin network side channels; the RGB and depth hierarchical features are input to the decoder, and the RGBD saliency detection results are output. The present invention adopts a twin network combined with a decoder network structure with a fusion function, performs feature fusion on hierarchical features and then decodes, so that RGB information and depth information complement each other, improve detection performance, and obtain refined RGBD detection results.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to an RGBD salient object detection method based on a twin network. Background technique [0002] Salient object detection intends to automatically detect areas or objects that human eyes focus on in images or scenes, and the detection results are called saliency maps, which can be used in various computer vision applications such as target detection and recognition, image compression, image retrieval, Content-based image editing. Although there are many salient object detection models and algorithms for RGB (that is, the input picture is a single RGB color image), for RGBD—that is, the input is a single RGB color image and its corresponding scene depth (Depth) map. Sexual object detection methods have been relatively lacking. With the increasing popularity of depth cameras such as Microsoft Kinect, Intel RealSense, and mobile phone depth cameras, the...

AI Technical Summary

Problems solved by technology

Chen et al. proposed "Progressively complementarity-aware fusion network for rgb-dsalient object detection" in 2018. This method uses two-way parallel neural networks (two-way parallel neural network structures are inconsistent, and parameters are not shared) for RGB and depth information. The features are extracted separately and then fused. Although the detection effect is better than the traditional method of manually designing features, the two-way parallel network structure increases the model parameters while making the whole model more complex, difficult to train, and not conducive to mining RGB. The commonality of the salient features between the map and the depth map, so the detection effect is limited in complex scenes

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