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Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Infrared small target detection occupies an important position in the infrared search and track system. The most common size of infrared images has developed to 640×512. The field-of-view (FOV) also increases significantly. As the result, there is more interference that hinders the detection of small targets in the image. However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. Owing to the locality of convolution kernels, recent convolutional neural networks (CNN) cannot model the long-range dependency in the image to suppress false alarms. In this paper, we propose a hierarchical vision transformer-based method for infrared small target detection in larger size and FOV images of 640×512. Specifically, we design a hierarchical overlapped small patch transformer (HOSPT), instead of the CNN, to encode multi-scale features from the single-frame image. For the decoder, a top-down feature aggregation module (TFAM) is adopted to fuse features from adjacent scales. Furthermore, after analyzing existing loss functions, a simple yet effective combination is exploited to optimize the network convergence. Compared to other state-of-the-art methods, the normalized intersection-over-union (nIoU) on our IRST640 dataset and public SIRST dataset reaches 0.856 and 0.758. The detailed ablation experiments are conducted to validate the effectiveness and reasonability of each component in the method.

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

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.

Spaceborne infrared small-target detection plays a critical role in space-sky early warning, disaster rescue, and reconnaissance tracking, benefiting from all-time, all-weather, and wide-area monitoring capabilities. The deployment of high-spatial-resolution infrared payloads (ground sampling distance, GSD < 10 m) has introduced pronounced scale diversity among targets, leading to size-sensitive performance degradation in existing detectors and heightened risks of missed detections or false alarms in mixed-size scenarios. Furthermore, multi-frame infrared small-target detection methods often face challenges in maintaining consistent temporal coherence during feature propagation across sequences. To overcome these limitations in high-resolution spaceborne infrared videos, we propose DTRFR, an end-to-end unified detection framework built on an enhanced recurrent feature refinement architecture. This approach incorporates a realistic SITP-QLSD dataset derived from QLSAT-2 infrared backgrounds, featuring diverse scenes, multi-size small targets, and a dedicated generalization sub-test set with extremely small targets partially unseen in training; a multi-scale IRFeatureExtractor leveraging parallel convolutions and dilated receptive fields for improved cross-scale discrimination and clutter suppression; and an adaptive gating pyramid deformable alignment module to optimize sequence alignment and enhance temporal consistency, enabling robust performance across various clutter levels and dynamic backgrounds. Extensive evaluations on SITP-QLSD demonstrate that DTRFR attains competitive performance, achieving mIoU of 74.32% and Pd of 94.51% on the main set, with strong robustness on the generalization sub-test set (Pd = 92.37%). Compared to single-frame and multi-frame baselines, the proposed method achieves higher detection accuracy with significantly reduced false alarms, benefiting from multi-scale feature extraction that enables robust detection of small targets of different sizes in infrared videos.

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.

Infrared small target detection holds significant importance across various domains, including military and security applications. Nevertheless, detecting small targets is highly challenging due to their minimal pixel presence in images, indistinct features, and complex backgrounds. Existing detection methods are often interfered by complex backgrounds, resulting in unsatisfactory detection results. To overcome this challenge, this study introduces the mixture of experts infrared small target detection (MOE‐IR) method. The core idea of this method is to construct a mixture of experts feature extraction network to perform rich feature extraction and complex background suppression on small targets, respectively, so as to achieve robust infrared small target detection in complex backgrounds. Specifically, the MOE‐IR comprises a target feature extraction expert and a background suppression expert. The target feature extraction expert focuses on enhancing the features of infrared small targets, while the background suppression expert aims to mitigate background clutter. Additionally, an adaptive gate controlled network is incorporated to dynamically assign weights to the two experts based on the input infrared image, ensuring effective detection of infrared small targets across diverse and complex scenarios. Extensive experiments demonstrate that the proposed algorithm surpasses existing infrared small target detection methods in terms of detection accuracy and false alarm rate. It can reliably and stably identify small targets within infrared images, thus offering an effective solution for practical infrared small target detection applications. A multi expert model‐based infrared small target detection method mixture of experts infrared (MOE‐IR) has been proposed to solve the problem of robust detection of infrared small targets in complex environments. In the MOE‐IR, the target feature extraction expert model and the background suppression expert model are proposed to suppress background clutter interference and extract rich infrared small target features, respectively. Meanwhile, an adaptive gate controlled network is designed to adaptively allocate the weights of the two expert models according to the input infrared image, so as to achieve effective detection of infrared small targets in different complex scenes. Comprehensive experiments demonstrate the detection performance of the proposed MOE‐IR in the public dataset SIRST is better than the existing advanced infrared small target detection methods, with Pd increased by 3% and Fa reduced by 73%.

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems.

In the context of small unmanned aerial vehicles (UAVs), infrared imaging faces challenges such as low quality, difficulty in detecting small targets, high false alarm rates, and computational resource constraints. To address these issues, we introduce AIMED-Net, an enhancing infrared small target detection net in UAVs with multi-layer feature enhancement for edge computing. Initially, the network encompasses a multi-layer feature enhancement architecture for infrared small targets, including a generative adversarial-based shallow-feature enhancement network and a detection-oriented deep-feature enhancement network. Specifically, an infrared image-feature enhancement method is proposed for the shallow-feature enhancement network, employing multi-scale enhancement to bolster target detection performance. Furthermore, within the YOLOv7 framework, we have developed an improved object detection network integrating multiple feature enhancement techniques, optimized for infrared targets and edge computing conditions. This design not only reduces the model’s complexity but also enhances the network’s robustness and accuracy in identifying small targets. Experimental results obtained from the HIT-UAV public dataset indicate that, compared to YOLOv7s, our method achieves a 2.5% increase in F1 score, a 6.1% rise in AP for detecting OtherVehicle targets, and a 2.6% improvement in mAP across all categories, alongside a 15.2% reduction in inference time on edge devices. Compared to existing state-of-the-art approaches, our method strikes a balance between detection efficiency and accuracy, presenting a practical solution for deployment in aerial edge computing scenarios.

To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.