# Advancing in RGB-D Salient Object Detection: A Survey
## 4 Performance Evaluation and Comparison
### Model Comparison  
This survey compared several prominent models from the past seven years, with detailed information provided in Table 2. Attention mechanisms, by emulating the human visual system's attention allocation, effectively enhance the model's ability to capture key information. Multimodal information fusion techniques integrate data from different sensors, providing richer contextual information for SOD tasks. The introduction of attention mechanisms and multimodal information fusion allows for balancing the importance of different regions or domains, thereby improving salient object detection performance. Various methods have demonstrated their strengths and limitations through different network structures and feature fusion strategies. For instance, SSRCNN performs well in dynamic scene analyses using recurrent neural networks but may face training complexity; PDNet enhances the detection of objects of varying sizes through multi-scale feature fusion but may increase computational burden; the contrast-prior feature pooling in CPFP is effective in high-contrast images but relies on image contrast; DCFNet introduces depth calibration and fusion strategies that effectively mitigate the noise and blur in raw depth images, leading to more accurate salient object detection; the cross-dimensional fusion network structure in C2 DFNet effectively combines RGB and depth information, improving detection accuracy, but requires high computational resources; the superpixel prototype sampling technique in SPSN reduces computational load while maintaining accuracy but is sensitive to initial segmentation quality; and the hierarchical dense attention mechanism in HiDAnet offers more refined feature learning and region localization but may lead to complex and time-consuming training and inference. Early models like PCF (2018) and SSRCNN (2019) made significant advances in feature fusion, but they may lack the ability to handle complex scenes compared to later models like CATNet (2023) or HFMDNet (2024), which incorporate more sophisticated depth calibration techniques to reduce the noise in depth data and improve detection accuracy in challenging environments. The evolution from models like TANet (2019) to EMTrans (2024) highlights the increasing complexity of handling noisy depth data. While TANet integrates depth information with limited robustness to noise, EMTrans leverages a transformer architecture to enhance spatial resolution and feature alignment between RGB and depth modalities, resulting in a more resilient detection process (particularly in environments with low-quality depth data). This evolution underscores how transformer-based models are reshaping the SOD landscape. As seen from Table 2, recent strategies in attention mechanisms and multimodal fusion include end-to-end deep learning models, attention mechanisms based on graph neural networks, multi-scale and multi-view fusion strategies, and the application of reinforcement learning in multimodal fusion. In terms of backbone architectures, there is a significant trend toward transitioning from traditional backbones like VGG to more advanced networks like ResNet, as well as transformer-based models such as Swin-B and PVT. These new architectures offer improved feature extraction capabilities and enhance overall model performance, particularly in terms of precision and recall, while reducing MAE. Additionally, the integration of attention mechanisms has become a standard approach for enhancing feature selection and increasing the model's focus on salient objects. These mechanisms work by dynamically directing the model's attention to the most relevant parts of the input data, thereby improving the model's ability to distinguish salient objects from background noise. The effective integration of RGB and depth data remains a key area of innovation. Technologies that excel in this domain, such as C2 DFNet and MVSalNet, demonstrate superior performance by leveraging the strengths of both modalities.