Guided multi-scale refinement network for camouflaged object detection

Xu, Xiuqi; Chen, Shuhan; Liu, Xiao; Wang, Jian; Hu, Xuelong

doi:10.1007/s11042-022-13274-4

Cited by 6 publications

(3 citation statements)

References 65 publications

(110 reference statements)

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“…Moreover, a boundary-guided fusion module was proposed to explore the complementary relationship between the camouflaged regions and their boundaries. Xu et al 43 proposed a guided multiscale refinement network for camouflaged object detection. They designed a global perception module for coarse localization by stacking a multiscale residual block on the top of the backbone in a recurrent manner.…”

Section: Camouflaged Target Detection and Recognitionmentioning

confidence: 99%

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Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

Zhao

Meng

et al. 2023

J. Electron. Imag.

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.Underwater bionic robots are very similar to real underwater creatures in appearance and features. This makes it difficult for autonomous underwater vehicles (AUVs) to distinguish their appearance features. Moreover, the existing underwater target detection methods have large model sizes and many parameters. This leads to difficulty in practical application of such UAV methods. To solve this, we propose an underwater camouflaged target recognition algorithm based on an adversarial graph structure. First, the salient features and environmental features of underwater images are fully explored by a custom salient feature extraction network and a related environmental feature extraction network. Second, the camouflaged target graph structure and the natural target graph structure of the fusion environment feature are constructed to obtain the dependency between the target and environment features. Finally, an adaptive frame regression camouflaged target detection network is constructed based on adversarial learning to recognize camouflaged targets and natural targets. In contrast to the existing target recognition model in the simulation of the four datasets of concealed target detection, quantitative underwater image dataset, underwater image enhancement benchmark dataset, and underwater image dataset, the proposed model gets higher average recognition accuracy of 0.820, which is an improvement of 3.4%, and the number of parameters is only 1.2 m. Proved by the experiments, the proposed target recognition algorithm is more effective and superior to the existing algorithms.

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Section: Camouflaged Target Detection and Recognitionmentioning

confidence: 99%

“…Moreover, a boundary-guided fusion module was proposed to explore the complementary relationship between the camouflaged regions and their boundaries. Xu et al 43 . proposed a guided multiscale refinement network for camouflaged object detection.…”

Section: Related Workmentioning

confidence: 99%

Adversarial learning-based method for recognition of bionic and highly contextual underwater targets

Zhao

Meng

et al. 2023

J. Electron. Imag.

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“…Wang et al (2021) presented a D2C‐Net composed of dual‐branch features extraction (DFE) and graded refined cross fusion (GRCF), which imitated the two‐stage detection process of the human visual mechanism when observing camouflage scenes. Afterward, Xu, Chen, et al (2023) developed a novel Guided Multi‐scale Refinement Network and used the global perception module for coarse localization to combat the similarity between complex scenes, low contrast, foreground, and background. More, CubeNet (Zhuge et al, 2022) is designed by introducing an X connection to the standard encoder‐decoder architecture, which makes the model the ability to provide powerful representations at each stage.…”

Section: Related Workmentioning

confidence: 99%

A military reconnaissance network for small‐scale open‐scene camouflaged people detection

Liu

2023

Expert Systems

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Although the work of identifying animal and plant objects with highly similar patterns (e.g., texture, intensity, colour, etc.) to the background has recently attracted more research interest but rarely involves the military complex environment. Design an efficient camouflage small‐scale object detection algorithm that is capable of quickly discriminating the objects in open scenes from a long distance, to pre‐empt the enemy. In this work, we first recognize the fact that existing open‐source training datasets are scarce, and we have created a specific disguised people benchmark covering multiple scenes and weather conditions. Second, because the severe corruption of camouflage capabilities and chaotic scenes in the open battlefield on detailed features and generalization intensifies the challenge of feature extraction from a long‐range perspective, we propose a novel end‐to‐end Small‐scale open scene Camouflage Object Detection Network, called SM‐CODN. Inspired by the characteristics of biological brain partition, a multi‐domain partition module (MPM) with domain‐decoupling is proposed to enable specific knowledge learning for samples with obvious discrepancies in camouflage domain distribution. Concurrent with our work, we have designed a multi‐scale fusion module (MFM) to strengthen the semantic features related to small‐scale disguised objects. Moreover, due to the convergence direction of the detector in reasoning being inconsistent, a feature separation enhancement module (FSEM) is also proposed. Experimental results show that SM‐CODN surpasses many classic object detection methods and shows strong competitiveness compared with state‐of‐the‐art ones.

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