A Fast Detection Method for Infrared Small Targets in Complex Sea and Sky Background

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI:10.1109/CACML55074.2022.00016

Z. Gao, Honghao Chang, Xiang-zheng Cheng, W. Liu, Xiaotian Wang, Wang Ying

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引用次数: 1

Abstract

The traditional infrared small target detection method is easy to cause false alarms in the complex sea and sky background. In this letter, a fast detection method for infrared small targets in the complex sea and sky background is proposed. Firstly, in order to make full use of the Gaussian characteristics of infrared small targets and effectively distinguish the target from the background clutter, the SODD (second order directional derivative) filter is used to improve the target signal-to-clutter ratio (SCR) and suppress the background clutter; Secondly, in order to improve the real-time performance of the algorithm, we improve the existing MPCM algorithm by reducing the difference dimension between the target area and the local background area; Finally, we use pipeline filtering that can take full advantage of candidate target location, size, and total grayscale 3D information to complete multi-frame target validation and achieve continuous target detection in image sequences. The experimental results show that the proposed algorithm can stably and accurately detect infrared small targets in the infrared image sequence.

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复杂海天背景下红外小目标的快速检测方法

传统的红外小目标检测方法在复杂的海天背景下容易产生虚警。本文提出了一种复杂海天背景下红外小目标的快速检测方法。首先，为了充分利用红外小目标的高斯特性，有效地将目标与背景杂波区分开来，采用SODD(二阶方向导数)滤波器提高目标信杂比，抑制背景杂波;其次，为了提高算法的实时性，我们对现有的MPCM算法进行了改进，减少了目标区域与局部背景区域之间的差维;最后，利用管道滤波，充分利用候选目标的位置、大小和总灰度三维信息，完成多帧目标验证，实现图像序列中的连续目标检测。实验结果表明，该算法能够稳定、准确地检测出红外图像序列中的红外小目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)

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