Unmanned aerial vehicles (UAVs) with thermal imaging cameras are widely used for target tracking, reconnaissance, and search operations. However, rapid thermal camera rotations during field-of-view adjustments introduce significant motion blur, impairing real-time image detection and tracking. While deep learning has been a dominant approach for image deblurring, its application to infrared image motion deblurring (IRMD) remains limited owing to the lack of publicly available datasets and challenges in handling large motion blur or maintaining real-time performance. This study addresses these gaps by constructing a large-scale UAV infrared motion deblurring (U2IRD) benchmark dataset, incorporating gyroscopic steering rate information. Additionally, we propose a gyro-aided spatial frequency network (GSFNet) that uses spatial and frequency domain features for UAV IRMD. The input data converts the gimbal steering rate information into a pixel distribution intensity map as a priori information. Specifically, the designed spatial depth residual attention module captures critical spatial domain details, while the multiple frequency domain feature recovery module extracts frequency domain features for effective deblurring. Extensive evaluations on U2IRD and synthetic thermal blurred image datasets demonstrate that the proposed method achieves state-of-the-art deblurring performance. The new IRMD dataset, available at https://github.com/aurora-sea/U2IRD, is anticipated to facilitate advancements in UAV IRMD research and applications.

Gravity anomalies including the gravity field and its gradient are governed by the 3D gravitational potential boundary value problem. When the gravity field vector and its gradient tensor are calculated from the gravitational potential boundary based on the integral solution or conventional numerical approaches, they will inevitably lose effectiveness and high accuracy, especially with 3D topography. In this letter, we introduce an efficient and accurate high-order spectral-element approach for simulating 3D gravity anomalies including surface topography. This new method is based on the spectral element technique of hexahedral grids to discretize the 3D Poisson equation of gravitational potential. To solve the gravitational potential variational problem that converted to the boundary value problem, we choose the Gauss-Lobatto-Legendre (GLL) quadrature in all discrete spectral elements, in which the interpolation and quadrature points are the same. A 3D topographic model is used to demonstrate the efficiency, accuracy, and flexibility of the proposed spectral-element approach in forward modeling of gravity anomalies. Numerical results show that our approach can provide an accurate approximation to the gravity field vector and the gravity gradient tensor.

The detection of small infrared targets with a low signal-to-noise ratio and low contrast in high-noise backgrounds is challenging due to the lack of spatial features of the targets and the scarcity of real-world datasets. Most existing methods are based on single-frame images, which are prone to numerous false alarms and missed detections. This paper proposes ST-Trans that provides an efficient end-to-end solution for the detection of small infrared targets in the complex context of sequential images. First, the detection of small infrared targets in complex backgrounds relying only on a single image has been significantly difficult due to the lack of available spatial features. The temporal and motion information of the sequence image was found to effectively improve target detection performance. Therefore, we used the C2FDark backbone to learn the spatial features associated with small targets, and the spatial-temporal transformer module to learn the spatiotemporal dependencies between successive frames of small infrared targets. This improved the detection performance in challenging scenes. Second, due to the lack of publicly available infrared small target sequence datasets for training, we annotated a set of small infrared targets for challenging scenes and published them as the sequential infrared small target detection (SIRSTD) dataset. Finally, we performed extensive ablation experiments on the SIRSTD dataset and compared its performance with that of state-of-the-art methods to demonstrate the superiority of the proposed method. The results revealed that ST-Trans outperformed other models and can effectively improve the detection performance for small infrared targets. The SIRSTD dataset is available at https://github.com/aurora-sea/SIRSTD.

The detection of small infrared targets with a low signal-to-noise ratios and contrasts in noisy and cluttered backgrounds is challenging and therefore a domain of active research. Traditional methods result in a large number of false alarms and missed detections. In the case of convolutional neural network-based methods, it may not be possible to identify deep small targets, or the details of the target’s edge contours may not be appropriately considered. Therefore, this paper proposes MSAFFNet to perform infrared small target detection based on an encoder-decoder framework. In the encoder stage, small target features are extracted using a resnet-20 backbone network, and the global contextual features of small targets are extracted using an atrous spatial pyramid pooling module. In the decoding stage, a dual-attention module is used to selectively enhance the spatial details of the target at the shallow level and representative features of the semantic information at the deep level. Multi-scale feature maps are then concatenated to achieve superior feature fusion. Additionally, multi-scale labels are constructed to focus on the details of the target contour and internal features based on edge information and an internal feature aggregation module. Experiments conducted on the NUAA-SIRST, NUDT-SIRST and XDU-SIRST datasets revealed that the proposed approach outperforms the representative methods and achieves an improved detection performance.

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.