Search Results (22)

Camouflaged object detection (COD) represents one of the most challenging tasks in computer vision, requiring sophisticated approaches to accurately extract objects that seamlessly blend within visually similar backgrounds. While contemporary techniques demonstrate promising detection performance, they predominantly suffer from computational complexity and resource requirements that severely limit their deployment in real-time applications, particularly on mobile devices and edge computing platforms. To address these limitations, we propose LiteCOD, an efficient lightweight framework that integrates local and global perceptions through holistic feature fusion and specially designed efficient attention mechanisms. Our approach achieves superior detection accuracy while maintaining computational efficiency essential for practical deployment, with enhanced feature propagation and minimal computational overhead. Extensive experiments validate LiteCOD’s effectiveness, demonstrating that it surpasses existing lightweight methods with average improvements of 7.55% in the F-measure and 8.08% overall performance gain across three benchmark datasets. Our results indicate that our framework consistently outperforms 20 state-of-the-art methods across quantitative metrics, computational efficiency, and overall performance while achieving real-time inference capabilities with a significantly reduced parameter count of 5.15M parameters. LiteCOD establishes a practical solution bridging the gap between detection accuracy and deployment feasibility in resource-constrained environments. Full article

Fruit segmentation is an essential task due to its importance in accurate disease prevention, yield estimation, and automated harvesting. However, accurate object segmentation in agricultural environments remains challenging due to visual complexities such as background clutter, occlusion, small object size, and color–texture similarities that lead to camouflaging. Traditional methods often struggle to detect partially occluded or visually blended fruits, leading to poor detection performance. In this study, we propose a context-aware segmentation framework designed for orchard-level mango fruit detection. We integrate multiscale feature extraction based on PVTv2 architecture, a feature enhancement module using Atrous Spatial Pyramid Pooling (ASPP) and attention techniques, and a novel refinement mechanism employing a Position-based Layer Normalization (PLN). We conducted a comparative study against established segmentation models, employing both quantitative and qualitative evaluations. Results demonstrate the superior performance of our model across all metrics. An ablation study validated the contributions of the enhancement and refinement modules, with the former yielding performance gains of 2.43%, 3.10%, 5.65%, 4.19%, and 4.35% in S-measure, mean E-measure, weighted F-measure, mean F-measure, and IoU, respectively, and the latter achieving improvements of 2.07%, 1.93%, 6.85%, 4.84%, and 2.73%, in the said metrics. Full article

Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with their background, presenting significant challenges such as low contrast, complex textures, and blurred boundaries. Existing deep learning methods often struggle to achieve robust segmentation under these conditions. To address these limitations, this paper proposes a novel COD network, SAM2-DFBCNet, built upon the SAM2 Hiera architecture. Our network incorporates three key modules: (1) the Camouflage-Aware Context Enhancement Module (CACEM), which fuses local and global features through an attention mechanism to enhance contextual awareness in low-contrast scenes; (2) the Cross-Scale Feature Interaction Bridge (CSFIB), which employs a bidirectional convolutional GRU for the dynamic fusion of multi-scale features, effectively mitigating representation inconsistencies caused by complex textures and deformations; and (3) the Dynamic Boundary Refinement Module (DBRM), which combines channel and spatial attention mechanisms to optimize boundary localization accuracy and enhance segmentation details. Extensive experiments on three public datasets—CAMO, COD10K, and NC4K—demonstrate that SAM2-DFBCNet outperforms twenty state-of-the-art methods, achieving maximum improvements of 7.4%, 5.78%, and 4.78% in key metrics such as S-measure ($S_{\alpha }$), F-measure ($F_{\beta }$), and mean E-measure ($E_{\varphi }$), respectively, while reducing the Mean Absolute Error (M) by 37.8%. These results validate the superior performance and robustness of our approach in complex camouflage scenarios. Full article

Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. Consequently, we propose ATDMNet, an amalgamated architecture combining CNN and transformer within a numerous phases feature extraction framework. ATDMNet employs Res2Net as the foundational encoder and incorporates two essential components: multi-head agent attention (MHA) and top-k dynamic mask (TDM). MHA improves local feature sensitivity and long-range dependency modeling by incorporating agent nodes and positional biases, whereas TDM boosts attention with top-k operations and multiscale dynamic methods. The decoding phase utilizes bilinear upsampling and sophisticated semantic guidance to enhance low-level characteristics, hence ensuring precise segmentation. Enhanced performance is achieved by deep supervision and a hybrid loss function. Experiments applying COD datasets (NC4K, COD10K, CAMO) demonstrate that ATDMNet establishes a new benchmark in both precision and efficiency. Full article

Small-Structure Camouflaged Object Detection (SSCOD) is a highly promising yet challenging task, as small-structure targets often exhibit weaker features and occupy a significantly smaller proportion of the image compared to normal-sized targets. Such data are not only prevalent in existing benchmark camouflaged object detection datasets but also frequently encountered in real-world scenarios. Although existing camouflaged object detection (COD) methods have significantly improved detection accuracy, research specifically focused on SSCOD remains limited. To further advance the SSCOD task, we propose a detail-preserving multi-scale adaptive network architecture that incorporates the following key components: (1) An adaptive scaling strategy designed to mimic human visual perception when observing blurry targets. (2) An Attentive Atrous Spatial Pyramid Pooling (A2SPP) module, enabling each position in the feature map to autonomously learn the optimal feature scale. (3) A scale integration mechanism, leveraging Haar Wavelet-based Downsampling (HWD) and bilinear upsampling to preserve both contextual and fine-grained details across multiple scales. (4) A Feature Enhancement Module (FEM), specifically tailored to refine feature representations in small-structure detection scenarios. Extensive comparative experiments and ablation studies conducted on three camouflaged object detection datasets, as well as our proposed small-structure test datasets, demonstrated that our framework outperformed existing state-of-the-art (SOTA) methods. Notably, our approach achieved superior performance in detecting small-structured targets, highlighting its effectiveness and robustness in addressing the challenges of SSCOD tasks. Additionally, we conducted polyp segmentation experiments on four datasets, and the results showed that our framework is also well-suited for polyp segmentation, consistently outperforming other recent methods. Full article

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high visual similarity between camouflaged objects and their surrounding environments. Traditional methods relying on the late-stage fusion of high-level semantic features and low-level visual features have reached a performance plateau, limiting their ability to accurately segment object boundaries or enhance object localization. This paper proposes the Cross-layer Semantic Guidance Network (CSGNet), a novel framework designed to progressively integrate semantic and visual features across multiple stages, addressing these limitations. CSGNet introduces two innovative modules: the Cross-Layer Interaction Module (CLIM) and the Semantic Refinement Module (SRM). CLIM facilitates continuous cross-layer semantic interaction, refining high-level semantic information to provide consistent and effective guidance for detecting camouflaged objects. Meanwhile, SRM leverages this refined semantic guidance to enhance low-level visual features, employing feature-level attention mechanisms to suppress background noise and highlight critical object details. This progressive integration strategy ensures precise object localization and accurate boundary segmentation across challenging scenarios. Extensive experiments on three widely used COD benchmark datasets—CAMO, COD10K, and NC4K—demonstrate the effectiveness of CSGNet, achieving state-of-the-art performance with a mean error (M) of $0.042$ on CAMO, $0.020$ on COD10K, and $0.029$ on NC4K. Full article

Camouflaged object segmentation refers to the task of accurately extracting objects that are seamlessly integrated within their surrounding environment. Existing deep-learning methods frequently encounter challenges in accurately segmenting camouflaged objects, particularly in capturing their complete and intricate details. To this end, we propose a novel method based on the Curiosity-Driven network, which is motivated by the innate human tendency for curiosity when encountering ambiguous regions and the subsequent drive to explore and observe objects’ details. Specifically, the proposed fusion bridge module aims to exploit the model’s inherent curiosity to fuse these features extracted by the dual-branch feature encoder to capture the complete details of the object. Then, drawing inspiration from curiosity, the curiosity-refinement module is proposed to progressively refine the initial predictions by exploring unknown regions within the object’s surrounding environment. Notably, we develop a novel curiosity-calculation operation to discover and remove curiosity, leading to accurate segmentation results. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing competitors on three challenging benchmark datasets. Compared with the recently proposed state-of-the-art method, our model achieves performance gains of $1.80\%$ on average for $S_{\alpha }$. Moreover, our model can be extended to the polyp and industrial defects segmentation tasks, validating its robustness and effectiveness. Full article

The task of camouflaged object segmentation (COS) is a challenging endeavor that entails the identification of objects that closely blend in with their surrounding background. Furthermore, the camouflaged object’s obscure form and its subtle differentiation from the background present significant challenges during the feature extraction phase of the network. In order to extract more comprehensive information, thereby improving the accuracy of COS, we propose a diffusion model for a COS network that utilizes frequency domain information as auxiliary input, and we name it FreDiff. Firstly, we proposed a frequency auxiliary module (FAM) to extract frequency domain features. Then, we designed a Global Fusion Module (GFM) to make FreDiff pay attention to the global features. Finally, we proposed an Upsample Enhancement Module (UEM) to enhance the detailed information of the features and perform upsampling before inputting them into the diffusion model. Additionally, taking into account the specific characteristics of COS, we develop the specialized training strategy for FreDiff. We compared FreDiff with 17 COS models on the four challenging COS datasets. Experimental results showed that FreDiff outperforms or is consistent with other state-of-the-art methods under five evaluation metrics. Full article

Camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most methods usually focus on information compensation, overlooking the difference between features, which is important for distinguishing the object from the background. To this end, we propose the cross-level iterative subtraction network (CISNet), which integrates information from cross-layer features and enhances details through iteration mechanisms. CISNet involves a cross-level iterative structure (CIS) for feature complementarity, where texture information is used to enrich high-level features and semantic information is used to enhance low-level features. In particular, we present a multi-scale strip convolution subtraction (MSCSub) module within CIS to extract difference information between cross-level features and fuse multi-scale features, which improves the feature representation and guides accurate segmentation. Furthermore, an enhanced guided attention (EGA) module is presented to refine features by deeply mining local context information and capturing a broader range of relationships between different feature maps in a top-down manner. Extensive experiments conducted on four benchmark datasets demonstrate that our model outperforms the state-of-the-art COD models in all evaluation metrics. Full article

The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models. Full article

Background and Objectives: Correction of lower face asymmetry still remains challenging in maxillofacial surgery. This report describes techniques for the lateral transposition of the symphyseal segment to restore lower face symmetry while maintaining gender-related features in cis- and transgender patients. Materials and Methods: A retrospective review of medical records of 31 patients who attended for esthetic corrective surgery after orthodontic camouflage or orthognathic treatment, or during facial feminization of the lower face between June 2021 and June 2023 was performed. Result: All patients underwent lateralization genioplasty (with or without advancement or setback), either with or without narrowing T-osteotomy supplemented with necessary procedures in order to obtain proper facial balance and desired esthetical effects, such as bichectomy, liposuction, and face and neck lift. The mean asymmetry of the chin was 5.15 mm and was surgically corrected either by single segment lateralization or T-shape narrowing genioplasty depending on the gender and esthetical requirements. No complications were reported. Conclusions: Lateral shift genioplasty serves as a powerful tool in primary and secondary corrective surgery for lower face asymmetry that maintains gender-specific facial features. It may serve either as an additive to orthodontic camouflage or a way to correct previous orthognathic surgery pitfalls. The surgeon performing esthetic genioplasty associated with gender-specific expectations must be trained in facelift and facial liposculpting techniques in order to provide the best results and properly choose the right procedures for the right patients. Full article

Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme (DDTDS) is proposed for segregating and identifying camouflaged objects. First, the image is segmented using textural pixel patterns for which the linear differentiation is performed. Convolutional neural learning is used for training the regions across pixel distribution and pattern formations. The neural network employs two layers for linear training and pattern differentiation. The differentiated region is trained for its positive rate in identifying the region around the target. Non-uniform patterns are used for training the second layer of the neural network. The proposed scheme pursues a recurrent iteration until the maximum segmentation is achieved. The metrics of positive rate, detection time, and false negatives are used for assessing the proposed scheme’s performance. Full article

The fusion of spectral–polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral–polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral–polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network’s ability to classify pixels. In order to test the algorithm’s performance, a spectral–polarimetric image dataset of artificial targets, named SPIAO (spectral–polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes. Full article

Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics ($S_{\alpha }$, $E_{\varphi }$, $F_{\beta }^w$, and $MAE$). Full article