一种基于深度学习的伪装目标检测模型

Yong Wang, Ling Li, Xin Yang, Xinxin Wang, Hui Liu
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引用次数: 10

摘要

伪装物体检测是一个困难的任务,因为它们的纹理与背景相似。本文的主要目的是探讨一个伪装目标检测问题,即对给定图像进行伪装目标的检测。尽管迷彩在军事目标探测和野生动物保护等方面有广泛的潜在应用,但这一问题尚未得到很好的研究。为了解决这一问题,提出了一种基于深度学习的伪装目标检测方法。该方法能够自动提取伪装目标的深层特征。它还能提供反映伪装效率的探测概率。实验结果表明,该深度学习方法可以有效检测不同场景,分别代表低、中、高伪装水平。
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A Camouflaged Object Detection Model Based on Deep Learning
Camouflaged object detection is a hard assignment due to their textures are similar to the background. The main intention of this paper is probe into a problem about the camouflaged object detection, that is, detecting its camouflaged object for a given image. This problem has not been well studied in spite of a large area of potential applications such as camouflage military targets detection and wildlife protection. To address this problem, a camouflage object detection method based on deep learning is proposed. The suggested method can detect camouflaged object which can extract deep features automatically. It can also provide detection probability which reflect camouflage efficiency. Experimental results show that the deep learning measure can effectively detect different scene, representing the camouflage level of low, medium and high respectively.
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