Construction of Semantic Segmentation Dataset of Camouflage Target Image

Xinyu, 梁新宇 Liang; Haokun, 林浩坤 Lin; Hui, 杨辉 Yang; Kaihong, 肖铠鸿 Xiao; Jichuan, 权冀川 Quan

doi:10.3788/lop202158.0410015

Cited by 3 publications

(1 citation statement)

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“…A public dataset, the military camouflage personnel dataset (MCPD), is selected for training. MCPD [13] mainly uses camouflage videos on the Internet as the original material and captures high-resolution camouflage human target images by video frame extraction.…”

Section: Experimental Data Set Productionmentioning

confidence: 99%

Soldier identification based on improved YOLOv5 algorithm in battlefield environment

Yawen,

Jie,

Bingqin

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Section: Experimental Data Set Productionmentioning

confidence: 99%

Soldier identification based on improved YOLOv5 algorithm in battlefield environment

Yawen,

Jie,

Bingqin

et al. 2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier

Jiang

Cai

Jiang

et al. 2022

Entropy

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In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model’s generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.

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Camouflage Target Segmentation Algorithm Using Multi-Scale Feature Extraction and Multi-Level Attention Mechanism

Liang¹,

Quan²,

Yang³

et al. 2022

J Comput-Aided Design Comput Graphics

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Construction of Semantic Segmentation Dataset of Camouflage Target Image

Cited by 3 publications

References 0 publications

Soldier identification based on improved YOLOv5 algorithm in battlefield environment

Soldier identification based on improved YOLOv5 algorithm in battlefield environment

MAGNet: A Camouflaged Object Detection Network Simulating the Observation Effect of a Magnifier

Camouflage Target Segmentation Algorithm Using Multi-Scale Feature Extraction and Multi-Level Attention Mechanism

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