一种基于深度学习的红外弱小目标检测改进方法

Tianwei Yang, Jungang Yang, W. An
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引用次数: 1

摘要

卷积网络是一种非常强大的视觉模型,可以用来检测图像中的物体。传统的目标检测框架一般分为基于锚点的目标检测器和无锚点的目标检测器。其中SSD是基于单级锚点的目标检测器,能够快速高效地检测到目标。为了检测红外弱小目标,我们利用改进的骨干网对SSD网络进行改进,以完成我们的目标检测任务。我们使用开放的无人机数据集,在开放数据集上实现了很高的训练和测试精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Method of Detecting Infrared Weak and Small Targets Based on Deep Learning
The convolution network is a very powerful visual model that can be used to detect objects in an image. Traditional target detection frameworks are generally divided into anchor-based object detector and anchor-free object detector. Among them, SSD is a single-stage anchor-based object detector that can detect objects quickly and efficiently. In order to detect the infrared weak and small objects, we improve the SSD network for our object detection tasks by using an improved backbone network. We use the open UAVs dataset and achieve highly training and testing accuracy in the open dataset.
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