Collaborative Prior-Enhanced RGB-D Salient Object Detection Network for Intelligent IoT Perception Devices

Although salient object detection (SOD) methods inspired by human attention mechanisms have received increasing attention for their superior performance, they suffer from a trade-off between efficacy and efficiency, due to the high performance generally coming at the expense of large parameter sizes and high computational costs. To address this issue, we propose a Collaborative Prior-Enhanced RGB-D Salient Object Detection Network (CPENet) for Intelligent IoT Perception Devices. Specifically, we propose a dual-stream architecture utilizing MobileViT as the backbone to tackle the challenge of capturing long-range dependencies among multi-modal features with reduced model complexity. Then, a Scale Information Enhancement Module (SIEM) is proposed to effectively enhance and fuse multi-scale features. To adaptively aggregate multi-modal features, we propose a Prior-Driven Modality Aggregation (PDMA) that integrates the coarse saliency maps (Prior-Glance) of model into cross-modal fusion. Furthermore, we propose an extremely simple shared decoder to both predict the saliency maps of fused features for deep supervision and generate Prior-Glance in generation modules. Finally, considering the deficiency of boundary representation, unlike other methods which append costly edge enhancement module, a prior-driven region loss is designed that improves the capacities of boundary representation and generalization of the model under the guidance of Prior-Region. Extensive experiments demonstrate that the proposed CPENet outperforms most state-of-the-art methods on seven datasets with fewer parameters (15.7M) and lower computational complexity (21.9GFLOPs). Codes and results are available at https://github.com/blossom-lv/CPENet.