Dim and small infrared target fast detection guided by visual saliency

It is always a difficult challenge to detect small infrared targets under complex background and ensure high detection rate and low false alarm rate. In this paper, a detection method based on morphological Top-hat filtering and local energy difference is proposed. This method utilizes the advantage that morphological Top-hat filter can suppress the backgTround, and then segmented the image based on local energy difference to identify small targets. In this paper, the specific detection process of this method is given, and a number of small infrared target grayscale images under complex background are selected for the simulation experiment. The experimental results show that this method has a high detection rate and a low false alarm rate, and is of certain reference value for the detection of small infrared targets under complex background.

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

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 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.

Infrared small target detection is one of the most important parts of infrared search and tracking (IRST) system. Generally, the small and dim target is of low signal-to-noise ratio and buried in the complicated background and heavy noise, which makes it extremely difficult to be detected with low false alarm rates. To solve this problem, we propose a small target detection method based on multiscale local contrast measure. Different from conventional methods, we novelly measure the local contrast from two aspects: local dissimilarity and local brightness difference. First, we present a new dissimilarity measure called the local energy factor (LEF) to describe the dissimilarity between the small targets and their surrounding backgrounds. Second, the feature of the brightness difference between the small targets and the backgrounds is utilized. Afterward, the local contrast is measured by taking both features of the above into account. Finally, an adaptive segmentation method is applied to extract the small targets from the backgrounds. Extensive experiments on real test data set demonstrate that our approach outperforms the state-of-the-art approaches.

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

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. However, IR small targets are very difficult to detect because of their weak brightness, small size, and lack of shape, structure, texture, and other information elements. In order to simultaneously satisfy the robustness and timeliness of target detection, inspired by density peak clustering and the human visual system, an idea combining an improved density peak global search and local contrast calculation is proposed. First, the positions of candidate targets are determined in the preprocessed image using the improved density peak global search method (IDPGSM). Second, the saliency map is obtained using the double-weights enhanced local contrast method (DWELCM) for the candidate target neighborhood. Finally, adaptive threshold segmentation is used to detect IR small targets. Through comprehensive analysis of five evaluation indicators, the experimental results on seven real sequences and three hundred IR images of different scenes that the proposed method has better detection performance compared with six baseline methods. It can quickly and accurately determine the small target position in the case of severe background clutter and noise interference.

A pixel-level local contrast measure for infrared small target detection

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.

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.

Effective detection of small infrared (IR) targets buried in complex backgrounds and heavy noise plays an important role in IR search and track (IRST) systems. In this letter, an IR small target detection method called adaptive scale patch-based contrast measure (ASPCM) is proposed. Compared with existing detection methods based on the human visual system (HVS), our method can estimate the size of the potential target at each location. According to the estimated size, the local contrast between the target and the background is greatly enhanced, and the background clutter can be further suppressed. Experimental results on four real sequences of different complex background demonstrate that the proposed method can detect targets effectively and efficiently, even in the case of complex backgrounds and heavy noises.

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

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

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>.