Review on recent development in infrared small target detection algorithms

Rawat, Sur Singh; Verma, Sashi Kant; Kumar, Yatindra

doi:10.1016/j.procs.2020.03.302

Cited by 75 publications

(35 citation statements)

References 50 publications

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“…Traditional infrared small target detection methods measure the discontinuity between a target and its background. Such methods based on single-frame detection include two-dimensional least mean square (TDLMS) adaptive filtering, top-hat filtering, and maximum mean filtering [1]. However, these methods are easily affected by background clutters and noise [25].…”

Section: Infrared Small Target Detectionmentioning

confidence: 99%

“…The detection of infrared small targets plays a critical role in infrared search and tracking systems, military early warning systems, remote sensing systems, and other applications owing to the ability of infrared radiation to penetrate obstacles such as fog and other atmospheric conditions and that of infrared sensors to capture images regardless of lighting conditions [1]. Such target detection systems employ passive sensors with low observability, and the all-weather, multi-scene nature of infrared imaging contributes to its suitability in a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

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EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

et al. 2021

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Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.

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Section: Infrared Small Target Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

et al. 2021

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“…Small infrared target detection technology, which has been widely used in several fields, such as military early warning, precision guidance, field rescue, and forest fire prevention, forms an essential part of an infrared search and tracking system [1]. Compared to other imaging methods, infrared imaging offers a longer range, resistance against interference, and independence from lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

et al. 2022

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The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.

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“…Infrared point target (IRPT) detection and tracking is an important and challenging aspect of study in infrared remote sensing, which was widely used in both the civil and military fields [ 1 , 2 , 3 , 4 , 5 ]. For instance, space debris and failed satellites pose a serious threat to the security of spacecraft.…”

Section: Introductionmentioning

confidence: 99%

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter

Jia

Rao

Zhang

et al. 2022

Sensors

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Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

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Review on recent development in infrared small target detection algorithms

Cited by 75 publications

References 50 publications

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter

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