A new method of infrared small target recognition

Dim and small infrared target fast detection guided by visual saliency

To detect small infrared targets under the condition of dense clutters, we propose a single-frame target detection algorithm based on a small bounding-box filter, which is characterized by good adaptability to the position and size of a small target. During the small target detection process, the proposed algorithm first searches for the local maximum gray pixel and then, a set of concentric bounding boxes whose center is the pixel found in the first step is constructed, and the detection thresholds of a neighboring region of this pixel are calculated based on the bounding boxes. Finally, the minimum threshold is used to detect small target pixels in the neighboring region. A fast version of the proposed algorithm is a minimum bounding-box filter, which can be implemented by dividing an image into blocks and using the mid-range and range to assess the concentration trend and dispersion of the background. Simulation and analysis results show that the proposed algorithm can achieve high detection probability and low false alarm rate when detecting small targets in the complex background; while its fast version has high computational efficiency. The proposed algorithm can be used in infrared searching and tracking systems.

The infrared (IR) small target detection algorithm with a high detection rate, low false alarm rate, and high real-time performance has significant application value in the field of IR remote sensing. IR small targets in complex backgrounds have low contrast and low signal-to-noise ratio (SNR). Therefore, small target detection is more difficult. Traditional IR small target detection is generally implemented by local contrast methods (LCM), nonlocal autocorrelation methods (NAM), and adaptive segmentation. In this letter, a robust infrared small target detection network (RISTDnet) is proposed based on deep learning. In RISTDnet, a feature extraction framework combining handcrafted feature methods and convolutional neural networks is constructed, a mapping network between feature maps and the likelihood of small targets in the image is established, and a threshold is applied on the likelihood map to segment real targets. Experimental results show that the RISTDnet can detect small targets with different sizes and low SNRs in complex backgrounds and have better effectiveness and robustness against existing algorithms.

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

Entropy-based window selection for detecting dim and small infrared targets

The infrared image in the sea background usually contains random noise, sea waves, fish scale light and other clutter. Sea waves and fish scale light interference are similar to small infrared targets, so it is difficult to eliminate such interference and detect small infrared targets quickly and accurately. In this paper, a small target detection method based on non-local similarity is proposed. The interference is eliminated by using the class feature similarity of interference and the overall feature uniqueness of the small target. Firstly, this paper performs filtering to remove random noise, and the suspected small targets can be extracted by executing Top Hat filtering. Secondly, a multi-feature vector is constructed for all suspected targets. Finally, this paper analyzes the characteristics of different interference and executes clustering step by step. The wave edge interference and fish scale light interference can be eliminated in turn. Feature threshold is set based on the interference that has been identified to obtain the true small infrared target according to the uniqueness of the characteristics of the true target. The experimental results show that compared with other small target detection algorithms, the proposed algorithm can detect small infrared targets in complex sea background effectively.

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.

Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios.

Infrared small target detection based on multi-perception of target features

Small infrared target detection is one of the key techniques in infrared searching and tracking applications. A novel small target detection method based on the complex filter bank is proposed in this paper. Firstly, a training sample dataset is built and the complex filter bank is utilized to extract the feature representation of each pixel. Then, the support vector machine is used to detect the small target. Finally, a simple adaptive segmentation method is adopted to segment the result image. To evaluate the performance of the proposed method, background clutter suppression and small target detection probability experiments are conducted over real infrared small targets in two image sequences. The experiment results show that the proposed method can not only suppress background clutter effectively, but also detect targets accurately with low false alarm rate.

At present there are some worm detection systems, primarily for a single LAN or with hardware router environment, which main use worm propagation characteristics for detection and has high false alarm rate, but it is not applicable for large-scale network for detecting. This paper presents a distributed worm detection technology, which is divided into two parts, client end and the console end program. The system uses the rule-based detection methods to monitor network worms, and the console side manages and coordinates the multiple side work of detecting. Experimental results show that the method can be good applicable for worms conduct surveillance based on a single or multiple local area network and is used for worm alarming, the method has high detection rate and low false alarm rate.

At present there are some worm intrusion detection systems, primarily for a single LAN or with hardware router environment, which are not applicable for large-scale network detection or have high false alarm rate by using only worm propagation characteristics for detection. This paper analyzed worm non-linear propagation models and drew out the worm transmission curves. Then a distributed worm detection technology is designed. The novel distributed worm detection system consists of two parts, client end and console end programs. The system uses rule-based detection method to monitor network worms, and the console side manages and coordinates detection work of the client sides. Experimental results show that the technology is a good solution to worm detection in multiple network environments which can give an alarm with high detection rate and low false alarm rate when the known worm appears.

Multi-stage morphological operators for small infrared target detection

High-precision detection of small targets with low signal-to-noise ratio in infrared (IR) images has great significance for IR precise guidance, video surveillance, etc. In this paper, we proposed a novel method called centre-surround high-boost filter-based improved local Weber contrast measure. First, centre-surround high-boost filter is proposed to fully utilize the grey difference information, which reduces the influence of the non-homogeneous background such as cumulus sky, sea-sky clutters and high brightness edge. Then, an improved local Weber contrast measure is used to construct the contrast operator of the ratio-difference joint form for further target contrast enhancement and arbitrary interference suppression. Finally, we apply an adaptive thresholding segmentation to find the true target. Experimental results on various cluttered background sequences demonstrate that our proposed method can detect small targets with higher detection rate and lower false alarm rate, and the performance is more stable than five comparable methods in diverse scenarios.

Infrared (IR) small-target detection plays a vital role in various applications of both civil and military areas, such as marine rescue, forest fire prevention, and precise guidance. However, challenges stemming from the small size of IR targets and noise interference in complex backgrounds often limit the accuracy of IR small-target detection algorithms. Owing to the rapid development of deep learning, numerous convolutional neural networks (CNNs)-based methods have emerged in recent years, but they are inevitable to encounter the risk of target loss in deep layers due to the use of pooling layers. To address this problem, we present a wavelet transform-based augmented perception network, namely WTAPNet, in this article. First, an enhancement and enlarge (EE) module is designed to improve the network’s perceptual capability for IR small targets by magnifying image resolution and augmenting target features at the same time. Then, a discrete wavelet transform-based downsampling (DWTD) module and an inverse wavelet transform-based fusion (IWTF) module are proposed. These two modules collaboratively work to extract and fuse multiscale features, which simultaneously reduces information loss. Finally, a bottom-up path fusion strategy is exploited to highlight and preserve small-target features, which associate the lowest level with the highest level features and interconnect predictions from different hierarchical levels. Experimental results on the NUDT-SIRST dataset and the SIRST dataset demonstrate the superiority of our WTAPNet over other state-of-the-art IR small-target detection methods in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>-measure, recall, and other indicators. Our codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MinjieWan/WTAPNet</uri>.