Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network

doi:10.3390/rs14153570

CIOMP OpenIR

	Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network
	D. Wu; L. H. Cao; P. J. Zhou; N. Li; Y. Li and D. J. Wang
	2022
发表期刊	Remote Sensing
卷号	14 期号:15 页码:31
摘要	Infrared small-target detection has widespread influences on anti-missile warning, precise weapon guidance, infrared stealth and anti-stealth, military reconnaissance, and other national defense fields. However, small targets are easily submerged in background clutter noise and have fewer pixels and shape features. Furthermore, random target positions and irregular motion can lead to target detection being carried out in the whole space-time domain. This could result in a large amount of calculation, and the accuracy and real-time performance are difficult to be guaranteed. Therefore, infrared small-target detection is still a challenging and far-reaching research hotspot. To solve the above problem, a novel multimodal feature fusion network (MFFN) is proposed, based on morphological characteristics, infrared radiation, and motion characteristics, which could compensate for the deficiency in the description of single modal characteristics of small targets and improve the recognition precision. Our innovations introduced in the paper are addressed in the following three aspects: Firstly, in the morphological domain, we propose a network with the skip-connected feature pyramid network (SCFPN) and dilated convolutional block attention module integrated with Resblock (DAMR) introduced to the backbone, which is designed to improve the feature extraction ability for infrared small targets. Secondly, in the radiation characteristic domain, we propose a prediction model of atmospheric transmittance based on deep neural networks (DNNs), which predicts the atmospheric transmittance effectively without being limited by the complex environment to improve the measurement accuracy of radiation characteristics. Finally, the dilated convolutional-network-based bidirectional encoder representation from a transformers (DC-BERT) structure combined with an attention mechanism is proposed for the feature extraction of radiation and motion characteristics. Finally, experiments on our self-established optoelectronic equipment detected dataset (OEDD) show that our method is superior to eight state-of-the-art algorithms in terms of the accuracy and robustness of infrared small-target detection. The comparative experimental results of four kinds of target sequences indicate that the average recognition rate P-avg is 92.64%, the mean average precision (mAP) is 92.01%, and the F1 score is 90.52%.
DOI	10.3390/rs14153570
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收录类别	sci ; ei
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66761
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	D. Wu,L. H. Cao,P. J. Zhou,et al. Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network[J]. Remote Sensing,2022,14(15):31.
APA	D. Wu,L. H. Cao,P. J. Zhou,N. Li,&Y. Li and D. J. Wang.(2022).Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network.Remote Sensing,14(15),31.
MLA	D. Wu,et al."Infrared Small-Target Detection Based on Radiation Characteristics with a Multimodal Feature Fusion Network".Remote Sensing 14.15(2022):31.

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