Camouflaged instance segmentation based on multi-scale feature contour fusion swin transformer

IF 3.4 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication Pub Date : 2025-04-09 DOI:10.1016/j.image.2025.117328

Yin-Fu Huang, Feng-Yen Jen

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引用次数: 0

Abstract

Camouflaged instance segmentation is the latest detection issue for finding hidden objects in an image. Since camouflaged objects hide with similar background colors, it is difficult to detect objects' existence. In this paper, we proposed an instance segmentation model called Multi-scale Feature Contour Fusion Swin Transformer (MFCFSwinT) consisting of seven modules; i.e., Swin Transformer as the backbone for feature extraction, Pyramid of Kernel with Dilation (PKD) and Multi-Feature Fusion (MFF) for multi-scale features, Contour Branch and Contour Feature Fusion (CFF) for feature fusion, and Region Proposal Network (RPN) and Cascade Head for bounding boxes and masks detection. In the experiments, four datasets are used to evaluate the proposed model; i.e., COCO (Common Objects in Context), LVIS v1.0 (Large Vocabulary Instance Segmentation), COD10K (Camouflaged Object Detection), and NC4K. Finally, the experimental results show that MFCFSwinT can achieve better performances than most state-of-the-art models.

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基于多尺度特征轮廓融合的旋转变压器伪装实例分割

伪装实例分割是寻找图像中隐藏对象的最新检测问题。由于伪装后的物体以相似的背景颜色隐藏，因此很难检测到物体的存在。本文提出了一种由7个模块组成的多尺度特征轮廓融合Swin变压器（MFCFSwinT）实例分割模型；即以Swin Transformer为主干进行特征提取，以PKD和MFF为主干进行多尺度特征提取，以轮廓分支和轮廓特征融合（CFF）为主干进行特征融合，以区域建议网络（RPN）和级联头（Cascade Head）进行包围盒和掩码检测。在实验中，使用了四个数据集来评估所提出的模型；即COCO （Common Objects in Context）、LVIS v1.0 （Large Vocabulary Instance Segmentation）、COD10K （camouflage Object Detection）和NC4K。最后，实验结果表明，MFCFSwinT可以获得比大多数最先进的模型更好的性能。

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来源期刊

Signal Processing-Image Communication 工程技术-工程：电子与电气

CiteScore

8.40

自引率

2.90%

发文量

138

审稿时长

5.2 months

期刊介绍： Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.