LSNet: Lightweight Spatial Boosting Network for Detecting Salient Objects in RGB-Thermal Images

Fusing infrared polarization images for road detection via denoising diffusion probabilistic models

Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods. In this Letter, we present a denoising diffusion model aimed at improving the performance of road detection in infrared polarization images. This model achieves effective integration of infrared intensity and polarization information through forward and reverse diffusion processes. Furthermore, we propose what we believe to be a novel method to augment polarized images from different orientations based on the angle of polarization. The augmented polarized image serves as the guiding condition, enhancing the robustness of the diffusion model. Our experimental results validate the effectiveness of the proposed method, demonstrating competitive performance compared to state-of-the-art methods, even with fewer training samples.

Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection

Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers pay attention to the triple-modal SOD task, namely the visible-depth-thermal (VDT) SOD, where they attempt to explore the complementarity of the RGB image, the depth image, and the thermal image. However, existing triple-modal SOD methods fail to perceive the quality of depth maps and thermal images, which leads to performance degradation when dealing with scenes with low-quality depth and thermal images. Therefore, in this paper, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet. Firstly, except for extracting features, the initial feature extraction subnet can generate a preliminary prediction map from each modality via a shrinkage pyramid architecture, which is equipped with the multi-scale fusion (MSF) module. Then, we design the weakly-supervised quality-aware region selection subnet to generate the quality-aware maps. Concretely, we first find the high-quality and low-quality regions by using the preliminary predictions, which further constitute the pseudo label that can be used to train this subnet. Finally, the region-guided selective fusion subnet purifies the initial features under the guidance of the quality-aware maps, and then fuses the triple-modal features and refines the edge details of prediction maps through the intra-modality and inter-modality attention (IIA) module and the edge refinement (ER) module, respectively. Extensive experiments are performed on VDT-2048 dataset, and the results show that our saliency model consistently outperforms 13 state-of-the-art methods with a large margin. Our code and results are available at https://github.com/Lx-Bao/QSFNet.

MSEDNet: Multi-scale fusion and edge-supervised network for RGB-T salient object detection

RGB-T Salient object detection (SOD) is to accurately segment salient regions in both visible light images and thermal infrared images. However, most of existing methods for SOD neglects the critical complementarity between multiple modalities images, which is beneficial to further improve the detection accuracy. Therefore, this work introduces the MSEDNet RGB-T SOD method. We utilize an encoder to extract multi-level modalities features from both visible light images and thermal infrared images, which are subsequently categorized into high, medium, and low level. Additionally, we propose three separate feature fusion modules to comprehensively extract complementary information between different modalities during the fusion process. These modules are applied to specific feature levels: the Edge Dilation Sharpening module for low-level features, the Spatial and Channel-Aware module for mid-level features, and the Cross-Residual Fusion module for high-level features. Finally, we introduce an edge fusion loss function for supervised learning, which effectively extracts edge information from different modalities and suppresses background noise. Comparative demonstrate the superiority of the proposed MSEDNet over other state-of-the-art methods. The code and results can be found at the following link: https://github.com/Zhou-wy/MSEDNet.

Lightweight Cross-Modal Information Mutual Reinforcement Network for RGB-T Salient Object Detection

RGB-T salient object detection (SOD) has made significant progress in recent years. However, most existing works are based on heavy models, which are not applicable to mobile devices. Additionally, there is still room for improvement in the design of cross-modal feature fusion and cross-level feature fusion. To address these issues, we propose a lightweight cross-modal information mutual reinforcement network for RGB-T SOD. Our network consists of a lightweight encoder, the cross-modal information mutual reinforcement (CMIMR) module, and the semantic-information-guided fusion (SIGF) module. To reduce the computational cost and the number of parameters, we employ the lightweight module in both the encoder and decoder. Furthermore, to fuse the complementary information between two-modal features, we design the CMIMR module to enhance the two-modal features. This module effectively refines the two-modal features by absorbing previous-level semantic information and inter-modal complementary information. In addition, to fuse the cross-level feature and detect multiscale salient objects, we design the SIGF module, which effectively suppresses the background noisy information in low-level features and extracts multiscale information. We conduct extensive experiments on three RGB-T datasets, and our method achieves competitive performance compared to the other 15 state-of-the-art methods.