Multi-Scale Feature Fusion Saliency Object Detection Based on RGB-D Images.

Purpose salient object detection is a basic problem in computer vision. At present, many saliency detection methods based on deep learning are based on the feature fusion of RGB images and depth maps according to the method of input fusion or result fusion, but these methods cannot effectively fuse of feature maps. In order to improve the performance of salient object detection algorithms, a multi-scale feature fusion RGB-D image salient object detection method is proposed. The main body of the model is designed as two feature encoders, two feature decoders and a cross-model multiscale feature interleaved fusion module. The two feature encoders correspond to the RGB image and the depth image respectively, which use the ResNet50 network pre-trained by the ImageNet dataset, the feature decoder is used to decode the output of the encoder in 5 different scales, and the cross-model multi-scale feature interleaved fusion module is used for the feature maps of different scales extracted by the decoder and encoder are fused, and the five- level fusion results are spliced and dimensionally reduced to output the final saliency prediction map. Experiments are compared with ten representative models in the past on four public significance data sets. Compared with the second-performing model, the S-measure of the model in this paper is increased by 0.391% on average on each data set., MAE is decreased by 0.330% on average, and F-measure is decreased by 0.405% on average. A multi- scale feature fusion model is proposed, which abandons the previous fusion method and uses feature fusion to interleave the shallow and deep features. Experiments show that the method proposed in this paper has stronger performance than previous methods, to achieve better results. [ABSTRACT FROM AUTHOR]