Optical remote sensing images (RSIs) have been widely used in many applications, and one of the interesting issues about optical RSIs is the salient object detection (SOD). However, due to diverse object types, various object scales, numerous object orientations, and cluttered backgrounds in optical RSIs, the performance of the existing SOD models often degrade largely. Meanwhile, cutting-edge SOD models targeting optical RSIs typically focus on suppressing cluttered backgrounds, while they neglect the importance of edge information which is crucial for obtaining precise saliency maps. To address this dilemma, this article proposes an edge-guided recurrent positioning network (ERPNet) to pop-out salient objects in optical RSIs, where the key point lies in the edge-aware position attention unit (EPAU). First, the encoder is used to give salient objects a good representation, that is, multilevel deep features, which are then delivered into two parallel decoders, including: 1) an edge extraction part and 2) a feature fusion part. The edge extraction module and the encoder form a U-shape architecture, which not only provides accurate salient edge clues but also ensures the integrality of edge information by extra deploying the intraconnection. That is to say, edge features can be generated and reinforced by incorporating object features from the encoder. Meanwhile, each decoding step of the feature fusion module provides the position attention about salient objects, where position cues are sharpened by the effective edge information and are used to recurrently calibrate the misaligned decoding process. After that, we can obtain the final saliency map by fusing all position attention cues. Extensive experiments are conducted on two public optical RSIs datasets, and the results show that the proposed ERPNet can accurately and completely pop-out salient objects, which consistently outperforms the state-of-the-art SOD models.

Recently, camouflaged object detection (COD), which suffers from numerous challenges such as low contrast between camouflaged objects and background and large variations of camouflaged object appearances, has received more and more concerns. However, the performance of existing camouflaged object detection methods is still unsatisfactory, especially when dealing with complex scenes. Therefore, in this paper, we propose a novel Decoupling and Integration Network (DINet) to detect camouflaged objects. Here, the depiction of camouflaged objects can be regarded as the iterative decoupling and integration of the body features and detail features, where the former focuses on the center of camouflaged objects and the latter contains pixels around edges. Concretely, firstly, we deploy two complementary decoder branches including a detail branch and a body branch to learn the decoupling features, namely body decoder features and detail decoder features. Particularly, each decoder block of the two branches incorporates features from three components, i.e. , the previous interactive feature fusion (IFF) module, adjacent encoder layers, and corresponding encoder layer. Besides, to further elevate the body decoder features, the body blocks also introduce the global contextual information, which is the combination of all body encoder features via the global context (GC) unit, to provide coarse object location information. Secondly, to integrate the two decoupling decoder features, we deploy the interactive feature fusion (IFF) module based on the interactive combination and channel attention. Following this way, we can progressively provide a complete and accurate representation for camouflaged objects. Extensive experiments on three public challenging datasets, including CAMO, COD10K, and NC4K, show that our DINet presents competitive performance when compared with the state-of-the-art models.

Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However,the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively. The code will be released at https://github.com/rmcong/FPNet_ACMMM23.

The optical remote sensing images (RSIs) show various spatial resolutions and cluttered background, where salient objects with different scales, types, and orientations are presented in diverse RSI scenes. Therefore, it is inappropriate to directly extend cutting-edge saliency detection methods for conventional RGB images to optical RSIs. Besides, the existing saliency models targeting RSIs often render imperfect saliency maps, where some of them are with coarse boundary details. To solve this problem, this article attempts to introduce the edge information to precisely detect salient objects in RSIs. Accordingly, we propose an edge-aware multiscale feature integration network (EMFI-Net) for salient object detection by conducting multiscale feature integration under the explicit and implicit assistance of salient edge cues. Specifically, our network contains two parts including the encoder and decoder. First, the encoder extracts multiscale deep features from three RSIs with different resolutions, where the high-level deep semantic features from three RSIs are integrated using a cascaded feature fusion module. Second, the encoder explicitly enriches the multiscale deep features by integrating the salient edge cues extracted by a salient edge extraction module. Meanwhile, we also implicitly deploy an edge-aware constraint to the supervision of the saliency map prediction by introducing a hybrid loss function. Finally, the decoder integrates the enriched multiscale deep features in a coarse-to-fine way, yielding a high-quality saliency map. The experiments conducted on two public optical RSI datasets clearly prove the effectiveness and superiority of the proposed EMFI-Net against the state-of-the-art saliency models.

The automated surface defect detection is a fundamental task in industrial production, and the existing saliency-based works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, which heavily limits the practical applications. To this end, we devise a multiscale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales, respectively. Particularly, the MI module can provide satisfactory characterization for defect regions with fewer parameters. Embarking on this module, we propose a lightweight multiscale interactive network (MINet) to conduct real-time salient object detection of strip steel surface defects. Comprehensive experimental results on SD-Saliency-900 dataset, which contains three kinds of strip steel surface defect detection images (i.e., inclusion, patches, and scratches), demonstrate that the proposed MINet presents comparable detection accuracy with the state-of-the-art methods while running at a GPU speed of 721 FPS and a CPU speed of 6.3 FPS for 368×368 images with only 0.28 M parameters.

Recently, more and more researchers have paid attention to the surface defect detection of strip steel. However, the performance of existing methods usually fails to detect the defect regions from some complex scenes, especially with the noise disturbance and diverse defect types. Therefore, this paper proposes an end-to-end Dense Attention-guided Cascaded Network (DACNet) to detect salient objects (i.e. defects) on the strip steel surface, where the proposed DACNet is a U-shape network including an encoder and a decoder. The encoder first deploys multi-resolution convolutional branches (i.e. high/ medium/ low) in a cascaded way. Concretely, the cascaded feature integration (CFI) unit fuses the deep features from the last convolutional blocks of multi-resolution branches, yielding the enhanced high-level deep semantic feature. Subsequently, coupled with the multi-level deep features from high-resolution branch, the new multi-scale deep features are capable of characterizing various defects. Then, driven by the dense attention mechanism which enables the deeper attention cues flow into decoding stages, the decoder progressively integrates the multi-scale deep features into the final saliency map, where the dense attention is designed to steer deep features pay more concerns to the defect regions. Comprehensive experiments are conducted on the public strip steel datasets, and the experimental results demonstrate that our model consistently outperforms the state-of-the-art models in all evaluation metrics.