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4D light field data record the scene from multiple views, thus implicitly providing beneficial depth cue for salient object detection in challenging scenes. Existing light field salient object detection (LF SOD) methods usually use a large number of views to improve the detection accuracy. However, using so many views for LF SOD brings difficulties to its practical applications. Considering that adjacent views in a light field are actually with very similar contents, in this work, we propose defining a more efficient pattern of input views, i. e., key sparse views, and design a network to effectively explore the depth cue from sparse views for LF SOD. Specifically, we firstly introduce a low rank-based statistical analysis to the existing LF SOD datasets, which allows us to conclude a fixed yet universal pattern for our key sparse views, including the number and positions of views. These views maintain the sufficient depth cue, but greatly lower the number of views to be captured and processed, facilitating practical applications. Then, we propose an effective solution with a key Complementary and Discriminative Interaction Module (CDIM) for LF SOD from key sparse views, named CDINet. The CDINet follows a two-stream structure to extract the depth cue from the light field stream (i. e., sparse views) and the appearance cue from the RGB stream (i. e., center view), generating features and initial saliency maps for each stream. The CDIM is tailored for inter-stream interaction of both these features and saliency maps, using the depth cue to complement the missing salient regions in RGB stream and discriminate the background distraction, to enhance the final saliency map further. Extensive experiments on three LF multi-view datasets demonstrate that our CDINet not only outperforms the state-of-the-art 2D methods, but also achieves competitive performance as compared with the state-of-the-art 3D and 4D methods. The code and results of our method are available at https://github.com/GilbertRC/LFSOD-CDINet.

Salient object detection (SOD) in optical remote sensing images (ORSIs) has become increasingly popular recently. Due to the characteristics of ORSIs, ORSI-SOD is full of challenges, such as multiple objects, small objects, low illuminations, and irregular shapes. To address these challenges, we propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. Specifically, TSCNet is based on the generic encoder-decoder structure. In addition to the encoder and decoder, TSCNet includes a vital Texture-Semantic Collaboration Module (TSCM), which performs valuable feature modulation and interaction on basic features extracted from the encoder. The main idea of our TSCM is to make full use of the texture features at the lowest level and the semantic features at the highest level to achieve the expression enhancement of salient regions on features. In the TSCM, we first enhance the position of potential salient regions using semantic features. Then, we render and restore the object details using the texture features. Meanwhile, we also perceive regions of various scales, and construct interactions between different regions. Thanks to the perfect combination of TSCM and generic structure, our TSCNet can take care of both the position and details of salient objects, effectively handling various scenes. Extensive experiments on three datasets demonstrate that our TSCNet achieves competitive performance compared to 14 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/TSCNet.

Recently, semantic segmentation based on RGB and thermal infrared (TIR) images has become a research hotspot because of its stability in the weak light environment. However, most of the current methods ignore the differences between the two modalities of data and do not use semantic information in multi-modal fusion. In this paper, we propose a novel Semantic-Guided Fusion Network (SGFNet) for RGB-Thermal semantic segmentation, which makes full use of semantic information in the multi-modal fusion. Our SGFNet consists of an asymmetric encoder with TIR branch and RGB branch and a decoder. We concentrate on enhancing the multi-modal feature representation in the encoder with a pattern of fusion and enhancement. Specifically, considering that TIR images are stable under weak light conditions, we first propose a Semantic Guidance Head to extract semantic information in the TIR branch. In the RGB branch, we propose a Multi-modal Coordination and Distillation Unit to fuse multi-modal features first. Then, we propose a Cross-level and Semantic-guided Enhancement Unit to enhance the fused features with cross-level information and semantic information. We arrange these two units at all stages of the RGB branch to generate features with strong representation abilities at different levels. For the decoder, to obtain large receptive fields and fine edges, we improve the Lawin ASPP decoder by introducing edge information extracted from the low-level features, proposing the edge-aware Lawin ASPP decoder. With our encoder and decoder working together, our SGFNet can identify objects accurately and segment objects finely. Extensive experiments on the MFNet dataset demonstrate the superior performance of the proposed SGFNet compared with state-of-the-art methods. The code and results of our method are available at https://github.com/kw717/SGFNet.

Compared with 2D image salient object detection (SOD), SOD in omnidirectional images (or 360° images) usually suffers from geometric distortion. Although existing omnidirectional image SOD (ODI-SOD) methods have improved the detection accuracy obviously, their application may be cumbersome in real scenes due to their high computational cost. To avoid distortion and reduce the computational cost simultaneously in ODI-SOD, we propose a novel lightweight distortion-aware network, named LDNet, in this letter. First, to extract features with less distortion from ODIs, we integrate the distortion-aware convolution and depth-wise separable convolution (DSConv) into distortion-aware DSConv (DDSConv) and replace the regular convolutions in the last two blocks of the ResNet-18 with DDSConvs to obtain our lightweight backbone network (LD-ResNet-18). To enhance spatial information in each channel of the extracted features at each level comprehensively, then, we propose a lightweight distortion-aware channel-wise enhancement (DCE) module (only 0.05M parameters) including DDSConvs with various dilation rates, channel shuffle operation and attention mechanism, and employ a high-to-low dense connection structure to modulate the enhanced multi-level features. Besides, we design a distortion-aware self-correlation (DSC) module (only 0.02M parameters) for mining the contextual dependency of the features via a coarse-fine strategy, and the correlated features are refined by DCE modules and integrated by another dense connection structure. The final saliency map is predicted from the densely integrated features. Compared with 12 state-of-the-art methods on two public datasets, our lightweight LDNet achieves competitive or even better performance with only 2.9M parameters and 3.4G FLOPs, which balances the efficiency and performance.

No-service rail surface defect (NRSD) segmentation is an essential way for perceiving the quality of no-service rails. However, due to the complex and diverse outlines and low-contrast textures of no-service rails, existing natural image segmentation methods cannot achieve promising performance in NRSD images, especially in some unique and challenging NRSD scenes, such as low illumination, chaotic background, multiple/tiny defects, and inconsistent defects. To this end, in this article, we propose a novel segmentation network for NRSDs based on normalized attention and dual-scale interaction, named NaDiNet. Specifically, NaDiNet follows the enhancement-interaction paradigm. The normalized channel-wise self-attention module (NAM) and the dual-scale interaction block (DIB) are two key components of NaDiNet. NAM is a specific extension of the channel-wise self-attention mechanism (CAM) to enhance features extracted from low-contrast NRSD images. The softmax layer in CAM will produce very small correlation coefficients which are not conducive to low-contrast feature enhancement. Instead, in NAM, we directly calculate the normalized correlation coefficient between channels to enlarge the feature differentiation. DIB is specifically designed for the feature interaction of the enhanced features. It has two interaction branches with dual scales, one for fine-grained clues and the other for coarse-grained clues. With both branches working together, DIB can perceive defect regions of different granularities. With these modules working together, our NaDiNet can generate an accurate segmentation map. Extensive experiments on the public NRSD-MN dataset with man-made and natural NRSDs demonstrate that our proposed NaDiNet with various backbones (i.e., VGG, ResNet, and DenseNet) consistently outperforms ten state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/NaDiNet.

Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i. e., encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR features or the properties of features at different levels. In this paper, we propose a novel feature fusion-based network for RGB-T semantic segmentation, named LASNet, which follows three steps of location, activation, and sharpening. The highlight of LASNet is that we fully consider the characteristics of cross-modal features at different levels, and accordingly propose three specific modules for better segmentation. Concretely, we propose a Collaborative Location Module (CLM) for high-level semantic features, aiming to locate all potential objects. We propose a Complementary Activation Module for middle-level features, aiming to activate exact regions of different objects. We propose an Edge Sharpening Module (ESM) for low-level texture features, aiming to sharpen the edges of objects. Furthermore, in the training phase, we attach a location supervision and an edge supervision after CLM and ESM, respectively, and impose two semantic supervisions in the decoder part to facilitate network convergence. Experimental results on two public datasets demonstrate that the superiority of our LASNet over relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/LASNet.

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this article, we propose a novel adjacent context coordination network (ACCoNet) to explore the coordination of adjacent features in an encoder–decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: 1) an encoder; 2) adjacent context coordination modules (ACCoMs); and 3) a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multilevel features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. In addition, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a bifurcation-aggregation block (BAB) to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/ACCoNet.

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote-sensing images (ORSI-SOD) are proposed. However, most methods ignore the number of parameters and computational cost brought by CNNs, and only a few pay attention to portability and mobility. To facilitate practical applications, in this article, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through $L_{2}$ loss and used for detail enhancement. Finally, starting from the highest level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods, but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76 M parameters and running with 1.7 G floating point operations (FLOPs) for $288 \times 288$ inputs. Our code and results are available at https://github.com/MathLee/SeaNet.