Hybrid Attention Asynchronous Cascade Network for Salient Object Detection

Haiyan Yang (), Yongxin Chen, Rui Chen and Shuning Liu
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Haiyan Yang: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Yongxin Chen: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Rui Chen: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Shuning Liu: School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China

Mathematics, 2023, vol. 11, issue 6, 1-13

Abstract: The highlighted area or object is defined as the salient region or salient object. For salient object detection, the main challenges are still the clarity of the boundary information of the salient object and the positioning accuracy of the salient object in the complex background, such as noise and occlusion. To remedy these issues, it is proposed that the asynchronous cascade saliency detection algorithm based on a deep network, which is embedded in an encoder–decoder architecture. Moreover, the lightweight hybrid attention module is designed to obtain the explicit boundaries of salient regions. In order to effectively improve location information of salient objects, this paper adopts a bi-directional asynchronous cascade fusion strategy, which generates prediction maps with higher accuracy. The experimental results on five benchmark datasets show that the proposed network HACNet is on a par with the state of the art for image saliency datasets.

Keywords: salient object detection; lightweight hybrid attention module; asynchronous cascading strategy; interactive residual module; parallel dilated convolution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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