@Article{JICS-17-102,
author = {Wang , Jian and Chen , Wenbing},
title = {LTDNet: A Lightweight Two-Stage Decoder Network for RGB-D Salient Object Detection},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {17},
number = {2},
pages = {102--117},
abstract = {<p style="text-align: justify;">Most existing models of RGB-D salient object detection (SOD) utilize heavy backbones like
VGGs and ResNets which lead to large model size and high computational costs. In order to improve this
problem, a lightweight two-stage decoder network is proposed. Firstly, the network utilizes MobileNet-V2 and
a customized backbone to extract the features of RGB images and depth maps respectively. In order to mine
and combine cross-modality information, cross reference module is used to fuse complementary information
from different modalities. Subsequently, we design a feature enhancement module to enhance the clues of the
fused features which has four parallel convolutions with different expansion rates. Finally, a two-stage decoder
is used to predict the saliency maps, which processes high-level features and low-level features separately and
then merges them. Experiments on 5 benchmark datasets comparing with 10 state-of-the-art models
demonstrate that our model can achieve significant improvement with smallest model size.</p>},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22353.html}
}