Highly Efficient RGB-D Salient Object Detection With Adaptive Fusion and Attention Regulation

Abstract

Existing RGB-D salient object detection (SOD) models have large numbers of parameters, high computational complexity, and slow inference speeds, limiting their deployment on edge devices. To address this issue, we propose a highly efficient network (HENet), focusing on developing lightweight RGB-D SOD models. Specifically, to fairly handle multimodal inputs and capture long-range dependencies of features, we employ a dual-stream structure and use MobileViT as the network encoder. We introduce the Adaptive Edge-Aware Fusion Module (AEFM) that adaptively adjusts the contribution of features during the fusion process based on the amount of feature information, and perceives the edges of the fused features at the pixel level. To compensate for the insufficient feature extraction capability of the lightweight backbone network, we propose the Dual-Branch Feature Enhancement Module (DFEM) to enhance the representation capability of the fused features. Finally, we design the Feature Attention Regulation Module (FARM) to adjust the model’s focus in real time. HENet has fewer parameters (11.9M) and lower computational complexity (10.7 GFLOPs), achieving an inference speed of 121 FPS for images with size $384\times 384$ . Extensive experiments are conducted on seven challenging RGB-D SOD datasets. The experimental results demonstrate that HENet outperforms 16 state-of-the-art methods and shows great potential in downstream computer vision tasks. Codes and results are available on https://github.com/BojueGao/HENet.