Multiple refinement and integration network for Salient Object Detection

Article type: Research Article

Authors: Dai, Chao | Pan, Chen^{; *} | He, Wei | Sun, Hanqi

Affiliations: Department of College of Information Engineering, China Jiliang University, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The purpose of the salient object detection (SOD) task is to suppress the background noise and segment the salient foreground regions. Some previous methods considered the strategies of background suppression and multi-level feature fusion. Other methods encountered the problem that single-scale convolution features are difficult to capture the correct object size. This paper reconsiders the above problems and proposes a comprehensive solution to achieve SOD for improving the detection performance and ensuring relatively fewer parameters. First, it is difficult to achieve a better refinement effect through only one refinement operation. To this end, a multi-scale denoising module (MSDM) and multi-pooling refinement module (MPRM) are proposed to jointly complete the refinement task of multi-level features. Besides, it is difficult to fully integrate complementary features through only one feature integration operation. Mutual learning module (MLM) is proposed to preliminarily integrate multi-level features. To reduce information redundancy, multi-attention (MA) mechanism is used to assist further integration. The proposed algorithm is called multiple refinement and integration network (MRINet). Experimental results on five benchmark datasets show that MRINet outperforms state-of-the-art methods on multiple evaluation metrics. Moreover, our ResNet-based algorithm only contains 25.202 million parameters, which is less than other ResNet-based algorithms and can run at over 37 fps on a single GPU. The code will be available at https://github.com/dc3234/MRINet.

Keywords: Deep learning, salient object detection, multi-scale denoising, multi-pooling refinement, mutual learning, multi-attention mechanism

DOI: 10.3233/AIC-210273

Journal: AI Communications, vol. 35, no. 1, pp. 31-44, 2022