Small target detection in infrared images using deep convolutional neural networks

IF 0.6 4区 物理与天体物理 Q4 OPTICS
Shuang Wu, Z. Zuo
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引用次数: 17

Abstract

A new deep convolutional network for detecting small targets in infrared images is proposed. The problem of small targets detection is transformed into the classification of small targets’location distribution. First,a Fully Convolutional Networks is used for enhancing and initially screening the small targets. After that,the original image and the background suppressed image are selected as the inputs for classification network which is used for the follow-up screening,and then the SEnet ( Squeeze-and-Excitation Networks) is used to select the feature maps. The experimental results show that the detection network is superior to multiple typical infrared small target detection methods and has an excellent result on different signal-to-noise ratio,different scenes and motion blur targets.
基于深度卷积神经网络的红外图像小目标检测
提出了一种新的用于红外图像小目标检测的深度卷积网络。将小目标检测问题转化为小目标位置分布的分类问题。首先,利用全卷积网络对小目标进行增强和初始筛选。然后,选择原始图像和背景抑制图像作为分类网络的输入,分类网络用于后续的筛选,然后使用SEnet (Squeeze-and-Excitation Networks)选择特征映射。实验结果表明,该检测网络优于多种典型的红外小目标检测方法,在不同信噪比、不同场景和运动模糊目标检测中均有较好的效果。
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来源期刊
CiteScore
1.20
自引率
14.30%
发文量
4258
审稿时长
2.9 months
期刊介绍:
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