The high computational cost of neural networks has prevented recent successes
in RGB-D salient object detection (SOD) from benefiting real-world
applications. Hence, this paper introduces a novel network, MobileSal, which
focuses on efficient RGB-D SOD using mobile networks for deep feature
extraction. However, mobile networks are less powerful in feature
representation than cumbersome networks. To this end, we observe that the depth
information of color images can strengthen the feature representation related
to SOD if leveraged properly. Therefore, we propose an implicit depth
restoration (IDR) technique to strengthen the mobile networks' feature
representation capability for RGB-D SOD. IDR is only adopted in the training
phase and is omitted during testing, so it is computationally free. Besides, we
propose compact pyramid refinement (CPR) for efficient multi-level feature
aggregation to derive salient objects with clear boundaries. With IDR and CPR
incorporated, MobileSal performs favorably against state-of-the-art methods on
six challenging RGB-D SOD datasets with much faster speed (450fps for the input
size of 320 $\times$ 320) and fewer parameters (6.5M). The code is released at
this https URL

本篇研究提出一种基于 Mobile 网络的 RGB-D 显著目标检测的方法，采用隐式深度恢复（IDR）技术和紧凑金字塔细化（CPR），并在 6 个 RGB-D SOD 数据集上表现出快速速度和更少的参数。