All in One: RGB, RGB-D, and RGB-T Salient Object Detection
Xingzhao Jia Zhongqiu Zhao Changlei Dongye Zhao Zhang
Salient object detection (SOD) aims to identify the most attractive objects
within an image. Depending on the type of data being detected, SOD can be
categorized into various forms, including RGB, RGB-D (Depth), RGB-T (Thermal)
and light field SOD. Previous researches have focused on saliency detection
with individual data type. If the RGB-D SOD model is forced to detect RGB-T
data it will perform poorly. We propose an innovative model framework that
provides a unified solution for the salient object detection task of three
types of data (RGB, RGB-D, and RGB-T). The three types of data can be handled
in one model (all in one) with the same weight parameters. In this framework,
the three types of data are concatenated in an ordered manner within a single
input batch, and features are extracted using a transformer network. Based on
this framework, we propose an efficient lightweight SOD model, namely AiOSOD,
which can detect any RGB, RGB-D, and RGB-T data with high speed (780FPS for RGB
data, 485FPS for RGB-D or RGB-T data). Notably, with only 6.25M parameters,
AiOSOD achieves excellent performance on RGB, RGB-D, and RGB-T datasets.