Multi-information guided camouflaged object detection

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Caijuan Shi , Lin Zhao , Rui Wang , Kun Zhang , Fanyue Kong , Changyu Duan
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引用次数: 0

Abstract

Camouflaged Object Detection (COD) aims to identify the objects hidden in the background environment. Though more and more COD methods have been proposed in recent years, existing methods still perform poorly for detecting small objects, obscured objects, boundary-rich objects, and multi-objects, mainly because they fail to effectively utilize context information, texture information, and boundary information simultaneously. Therefore, in this paper, we propose a Multi-information Guided Camouflaged Object Detection Network (MIGNet) to fully utilize multi-information containing context information, texture information, and boundary information to boost the performance of camouflaged object detection. Specifically, firstly, we design the texture and boundary label and the Texture and Boundary Enhanced Module (TBEM) to obtain differentiated texture information and boundary information. Next, the Neighbor Context Information Exploration Module (NCIEM) is designed to obtain rich multi-scale context information. Then, the Parallel Group Bootstrap Module (PGBM) is designed to maximize the effective aggregation of context information, texture information and boundary information. Finally, Information Enhanced Decoder (IED) is designed to effectively enhance the interaction of neighboring layer features and suppress the background noise for good detection results. Extensive quantitative and qualitative experiments are conducted on four widely used datasets. The experimental results indicate that our proposed MIGNet with good performance of camouflaged object detection outperforms the other 22 COD models.
多信息制导伪装目标探测
伪装目标检测(COD)的目的是识别隐藏在背景环境中的目标。尽管近年来提出了越来越多的COD方法,但现有方法在检测小目标、模糊目标、富含边界的目标和多目标方面仍然表现不佳,主要原因是它们未能有效地同时利用上下文信息、纹理信息和边界信息。因此,本文提出了一种多信息引导的伪装目标检测网络(MIGNet),充分利用包含上下文信息、纹理信息和边界信息的多信息来提高伪装目标检测的性能。具体而言,首先设计纹理和边界标签以及纹理和边界增强模块(TBEM),以获得区分的纹理信息和边界信息;其次,设计邻域上下文信息探索模块(NCIEM),获取丰富的多尺度上下文信息。然后,设计并行组引导模块(PGBM),最大限度地实现上下文信息、纹理信息和边界信息的有效聚合;最后,设计了信息增强解码器(Information Enhanced Decoder, IED),有效增强相邻层特征之间的相互作用,抑制背景噪声,获得良好的检测效果。在四个广泛使用的数据集上进行了大量的定量和定性实验。实验结果表明,我们提出的MIGNet具有良好的伪装目标检测性能,优于其他22种COD模型。
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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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