基于扩张卷积条件生成对抗网络的红外小目标检测

Infrared Small Target Detection Based on Dilated Convolutional Conditional Generative Adversarial Networks

基于深度神经网络的目标检测方法凭借自身强大的建模能力,在通用目标检测任务中取得了良好的表现。然而,在红外小目标信号弱、像素小的本质特征的影响下,深度神经网络层次的加深和池化操作的大量使用导致小目标语义信息丢失,使得现有方法的检测效果并不理想。文中从红外小目标特性这一关键问题出发,提出了一种新颖的基于扩张卷积条件生成对抗网络的目标检测算法。所提方法应用扩张卷积设计了生成网络,充分利用上下文信息建立层与层之间的关联,将红外小目标更多的语义信息保留到深层网络中,增强目标特征,进而提高检测性能。此外,设计了融合通道与空间维度的混合注意力模块,在特征提取时有选择性地放大目标信息,抑制背景信息;设计了自注意关联模块处理层与层之间信息融合过程中产生的语义冲突问题。文中使用多种评价指标将所提网络模型与目前先进的其他红外小目标检测方法进行对比,证明了该方法在复杂背景下目标检测性能的优越性。在公开的SIRST数据集上,所提模型的F分数为64.70%,相比传统方法提高了8.29%,相比深度学习方法提高了7.29%;在公开的ISOS数据集上,所提模型的F分数为64.54%,相比传统方法提高了23.59%,相比深度学习方法提高了6.58%。

Deep-learning based object detection methods have achieved great performance in general object detection tasks by virtue of their powerful modeling capabilities.However,the design of deeper network and the abuse of pooling operations also lead to semantic information loss which suppress their performance when detecting infrared small targets with low signal-noise-ratio and small pixel essential features.This paper proposes a novel infrared small target detection algorithm based on dilated convolution conditional generative adversarial network.A dilated convolution stacked generative network makes full use of context information to establish layer-to-layer correlations and facilitate semantic information retainment of infrared small targets in the deep network.In addition,the generative network integrates the channel-space-mixed attention module which selectively amplifies target information and suppresses background clusters.Furthermore,a self-attention association module is proposed to deal with semantic conflict generated during the fusion process between layers.A variety of evaluation metrics are used to compare the proposed method with other state-of-the-arts at present to demonstrate the superiority of the proposed method in complex backgrounds.On the public SIRST dataset,the F score of the proposed model is 64.70%which is 8.29%higher than the traditional method and 7.29%higher than the deep learning method.On the public ISOS dataset,the F score is 64.54%,which is 23.59%higher than the traditional method and 6.58%higher than the deep learning method.