基于全卷积网络的红外弱小目标检测算法  

Small Infrared Target Detection Based on Fully Convolutional Network

作　　者：杨其利 周炳红 郑伟 李明涛 YANG Qili;ZHOU Binghong;ZHENG Wei;LI Mingtao(National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家空间科学中心,北京100190 [2]中国科学院大学,北京100049

出　　处：《红外技术》2021年第4期349-356,共8页Infrared Technology

基　　金：北京市重大科技专项(Grant Z181100002918004)。

摘　　要：在小天体探测、导弹制导和战场侦察等航空航天领域,由于目标信号较弱,占有像素数少,缺少目标形状和纹理信息,使用手工特征提取的传统算法容易出现大量虚警,而拥有强大特征提取能力的深度学习算法无法对微小且缺乏轮廓信息的目标训练。本文采用了滑动窗口取样训练,它源自基于人类视觉特性的传统目标检测算法中嵌套结构的思想,设计了一种使用递归卷积层的全卷积网络,在不增加额外训练参数的情况下,扩展了模型的网络深度,该网络的并行卷积结构的多个分支网络模拟了传统算法的多尺度操作,有利于在复杂环境中增强目标和背景之间的对比度,并且设计使用了多种损失函数的组合,以对抗正负样本严重不平衡的问题。实验结果表明:该方法实现了比传统方法更好的检测效果,为此领域的研究者们提供了一个新的思路和解决途径。In the field of aerospace research,such as in small celestial body detection,missile guidance,and battlefield reconnaissance,because the target signal is weak,the number of pixels occupied is small,and the target lacks shape structure and texture information,traditional algorithms with manual feature extraction are prone to false alarms,whereas deep learning methods with powerful feature extraction capabilities cannot train tiny targets that lack contour information.In this context,a sliding window sampling training method is adopted,which originates from the idea of nested structures in traditional algorithms based on human visual characteristics.A fully convolutional network using recursive convolutional layers is designed to extend the depth of the network without increasing the training parameters.The multi-branch structure of the network’s parallel convolution structure simulates the multi-scale operation of the traditional algorithm,which can enhance the contrast between the target and the background.Additionally,various loss functions are designed to combat the serious imbalance between positive and negative samples.The results show that the algorithm achieves a better detection performance than the traditional algorithms.

关 键 词：红外图像 弱小目标检测 递归卷积 全卷积网络 背景抑制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]