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Liu et al. Intell Robot 2024;4(4):503-23 Intelligence & Robotics
DOI: 10.20517/ir.2024.29

Research Article Open Access

SANet: scale-adaptive network for lightweight salient
object detection

Zhuang Liu, Weidong Zhao, Ning Jia, Xianhui Liu, Jiaxiong Yang
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.

Correspondence to: Prof. Weidong Zhao, College of Electronics and Information Engineering, Tongji University, No. 4800, Cao’an
Highway, Jiading District, Shanghai 201804, China. E-mail: zhaowd494@163.com

How to cite this article: Liu Z, Zhao W, Jia N, Liu X, Yang J. SANet: scale-adaptive network for lightweight salient object detection.
Intell Robot 2024;4(4):503-23. http://dx.doi.org/10.20517/ir.2024.29
Received: 18 Sep 2024 First Decision: 19 Nov 2024 Revised: 17 Dec 2024 Accepted: 19 Dec 2024 Published: 31 Dec 2024

Academic Editor: Simon Yang Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Salient object detection (SOD) is widely used in transportation such as road damage detection, assisted driving, etc.
However, heavyweight SOD methods are difficult to apply in scenarios with low computing power due to their huge
amount of computation and parameters. The detection accuracy of most lightweight SOD methods is difficult to meet
application requirements. We propose a novel lightweight scale-adaptive network to achieve a trade-off between
lightweight restriction and detection performance. We first propose the scale-adaptive feature extraction (SAFE)
module, which mainly consists of two parts: multi-scale feature interaction, which can extract features of different
scales and enhance the representation ability of the network; and dynamic selection, which can adaptively assign
different weights to features of varying scales according to their contribution through the input image. Then, based
on the SAFE module, a lightweight and adaptive backbone network is designed, and scale-adaptive network is imple-
mented in combination with the multi-scale feature aggregation (MFA) module. We evaluate the model quantitatively
and qualitatively on six public datasets and compare it with typical heavyweight and lightweight methods. With only
2.29 M parameters, it can achieve a prediction speed of 62 fps on a GTX 3090 GPU, far exceeding other models,
and real-time performance is guaranteed. The model performance reaches that of general heavyweight methods and
exceeds state-of-the-art lightweight methods.

Keywords: Salient object detection, lightweight SOD, model lightweighting, multi-scale learning

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
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