Visualizing real-time multivariate data using preattentive processing

Both visible and infrared images are important sources of intelligence information on the battlefield, and air‐to‐ground reconnaissance by UAV is an important means to obtain intelligence. However, there are great challenges in ground target detection and tracking, especially in complex battlefield environments. Aiming at the problem of insufficient accuracy of target detection by a single type of sensor in the battlefield environment at this stage, a target detection method by fusion of visible and infrared images is proposed in this paper, which is called ReconnaissanceFusion‐YOLO (RF‐YOLO), and with the help of infrared imagery, it can effectively improve the accuracy of target detection in the case of insufficient light. The performance of target detection in the battlefield is significantly improved by introducing two key innovative modules: dual feature fusion (DFF) module and feature fusion corrector (FFC) module. The DFF module enhances multi‐channel feature fusion through a novel concatenation and channel‐wise attention mechanism, while the FFC module performs feature correction between parallel streams using spatial and channel‐wise weights, addressing noise and uncertainty in different modalities. These modules are integrated on top of a dual‐stream YOLO architecture, allowing for effective fusion of visible and infrared information. RF‐YOLO was trained and evaluated using the FLIR data set, containing 5142 pairs of strictly aligned visible and infrared images. Results demonstrate that RF‐YOLO significantly outperforms benchmark networks in terms of robustness requirements. Specifically, the large model of RF‐YOLO achieves an mAP of 0.831, which is a significant improvement compared to the YOLOv5l inf benchmark's 0.739. This represents an improvement of over 12% in detection accuracy. Additionally, RF‐YOLO offers a Nano version that balances accuracy and speed. The Nano version achieves an mAP of 0.765, while maintaining a model size of only 11.5 MB, making it suitable for deployment on UAV edge computing devices with limited resources. To validate the practical applicability of our approach, this paper successfully implements target detection and tracking on a real UAV's edge computing device using the ROS system and SiameseRPN, combined with the proposed RF‐YOLO. Real‐world flight tests were conducted on an internal playground, demonstrating the effectiveness of our method in actual UAV applications. The system achieved a processing rate of approximately 10 fps at 640 × 640 resolution on an NVIDIA TX2 edge computing device, showcasing its real‐time performance capability in practical scenarios. This study contributes to enhancing UAV‐based battlefield reconnaissance capabilities by improving the accuracy and robustness of target detection and tracking in complex environments. The proposed RF‐YOLO method, along with its successful implementation on a real UAV platform, provides a promising solution for advanced military intelligence gathering and decision‐making support.

Accurate and reliable target detection is a crucial requirement of high-frequency surface wave radar for effective maritime surveillance. However, existing methods based on single-frame radar images primarily focus on static target features, limiting their ability to capture dynamic ship behaviors across multiple frames. In this study, a two-stage multiframe target detection framework (MFTDF) is proposed to address this issue in ship-target detection from range-Doppler (RD) images. The framework consists of two stages: a space-to-depth YOLOv8 network in Stage-1 and a multiframe vision transformer network in Stage-2. First, Stage-1 aims to extract regions of interest (ROI) from the current and several preceding RD images. Thereafter, target association and pattern selection are applied to collect multiframe image patch sequences for each ROI result. Finally, Stage-2 focuses on further discriminating the input image patches to obtain refined target detection results. Moreover, the dataset for training and validating the two-stage network is automatically generated based on the automatic identification system ship data and constant false alarm rate detection results to identify all true and visible targets, ensuring the dataset's credibility. Experiments using the measured data show that the proposed MFTDF achieves a considerably improved precision rate while maintaining an average improvement of 12.3% in the recall rate. These results confirm that MFTDF delivers superior detection accuracy and efficiency, offering a robust solution for maritime target detection in complex scenarios.

As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic Aperture Radar (SAR) images. For CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. We have developed a new target detection approach in which clutter is modeled using a Rayleigh mixture model that fits well to a variety of high-resolution SAR imagery. For computational efficiency, we use summed area tables (SAT) for computing background statistics. The resulting approach, called RmSAT-CFAR, is a promising general-purpose SAR target detection tool. This paper describes the open-source software for RmSAT-CFAR. Details of RmSAT-CFAR is published in the study named Fast Target Detection in Radar Images using Rayleigh Mixtures and Summed Area Tables. In addition to Rayleigh mixture and SATs, the software also uses tiling and parallelization to obtain faster and scalable results. This software repository also contains open source implementations for following algorithms: (a) Cell Averaging CFAR (CA-CFAR), (b) Automatic Censored CFAR (AC-CFAR), and (c) Adaptive and Fast CFAR (AAF-CFAR).

Hyperspectral sensors provide high spectral resolution, enabling accurate material discrimination and effective target detection. However, their practical use is constrained by limited spatial resolution and high acquisition costs. This paper proposes a novel framework to enhance small-target detection in multispectral imagery by leveraging deep learning-based spectral reconstruction to generate high-resolution hyperspectral representations from multispectral inputs. Two state-of-the-art reconstruction networks, MST++ and MIRNet, are trained using paired multispectral–hyperspectral samples derived from AVIRIS-NG data through proper spectral response functions. To improve discriminative capability for the target of interest, a rapid, target-specific fine-tuning stage is introduced, allowing the models to adapt to spectral signatures that are poorly represented or absent in the original training data. Target detection is performed using a spectral signature-based detector applied to the reconstructed hyperspectral data. The proposed framework is evaluated in a real-world scenario involving known field-deployed targets and hyperspectral imagery acquired from an unmanned aerial vehicle. Experimental results demonstrate that the proposed approach significantly outperforms baseline detection applied directly to multispectral data. These findings underscore the effectiveness of spectral reconstruction for downstream tasks such as target detection, particularly in scenarios where hyperspectral data are expensive or unavailable.

A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

Target heat-map network: An end-to-end deep network for target detection in remote sensing images

Real-time performance and accuracy are the standards to measure the detection algorithm performance. The existing detection methods for forward-looking sonar target image are difficult to achieve ideal balance between the two standards. Due to the influence of background interference and non-rigid deformation, it hardly obtains the high detection accuracy in fast detection. In this paper, we propose a Mobilenetv3-YOLOv4-Sonar algorithm, which is a more accurate and lightweight target detection model. The backbone of YOLOv4 adopts Mobilenetv3, which is improved by Convolutional Block Attention Module (CBMA) and modified Squeeze-and-Excitation Network (SENet). Consequently, we can eliminate the influence of high-light background interference and reduce the complexity of the model. Meanwhile, the improved SPPnet is used as the neck of YOLOv4 to reduce the failure of target detection caused by non-rigid deformation. Extensive experiments and comparisons with state-of-arts detectors illustrate that the accuracy of our model has increased by 4.6% compared with YOLOv4 on the dataset, and the model parameters are only one tenth of the traditional YOLOv4, which has practical significance for target detection in forward-looking sonar images.

As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

Unmanned aerial vehicles (UAVs) become increasingly available devices with extensive usage as environmental monitoring systems. With the benefit of higher mobility, UAVs are applied to fuel various significant uses in computer vision (CV), providing more effectiveness and accessibility than surveillance cameras with permanent camera view, angle, and scale. Nevertheless, owing to camera motion and composite environments, it is problematic to identify the UAV; conventional models frequently miss UAV detection and make false alarms. Drone-equipped cameras monitor objects at changing altitudes, leading to essential scale variants. The model increases targeted accuracy and decreases false positives using real-time data and machine learning (ML) methods. Its enormous applications range from military operations to urban planning and wildlife monitoring. Therefore, this study develops a novel long-term target detection and tracking model for unmanned aerial vehicles using a deep fusion-based convolutional transform learning (LTTDT–UAVDFCTL) model. The LTTDT–UAVDFCTL model presents a new model to improve the robustness and accuracy of target tracking and detection in scale-variant environments. At first, the presented LTTDT–UAVDFCTL technique performs image pre-processing by utilizing the median median-enhanced wiener filter (MEWF) technique to improve clarity and reduce noise. For object detection (OD), the highly accurate YOLOv8 technique is utilized, followed by feature extraction through a backbone deep fusion-based convolutional transform learning of VGG16, CapsNet, and EfficientNetB7 to capture both spatial and hierarchical features across varying scales. Moreover, the graph convolutional neural network (GCN) technique is employed for long-term target detection and tracking models. Finally, the hybrid nonlinear whale optimization algorithm with sine cosine (SCWOA) is implemented for the optimum choice of the hyperparameters involved in the GCN technique. The experimental study of the LTTDT–UAVDFCTL approach is performed under the VisDrone dataset. The performance validation of the LTTDT–UAVDFCTL approach portrayed a superior mAP value of 80.13% over existing models.

Ground-penetrating radars (GPR) based on a variety of techniques have been proposed to improve the performance of buried target (e.g., landmines, threat devices) detection. However, the small radar cross section (RCS) of small electronic devices poses difficulties for target detection, especially when they are buried in lossy and inhomogeneous media. This paper presents a novel buried nonlinear target detection method based on the decomposition of the time-reversal operator (DORT) that uses a multistatic system to overcome the limitations of conventional GPR. Using harmonic radar, which detects the harmonic responses scattered from electronic devices, and DORT processing, which enables focusing/imaging of the detected target, the detection performance is verified by conducting simulation and measurements. The overall results demonstrate that the proposed method achieves accurate detection of buried targets with small RCS.

In recent years, in the field of remote sensing image target detection, oriented target detection has received extensive attention. Traditional detectors use horizontal bounding box (HBB) to locate targets, which may cause inaccurate detection when detecting targets with arbitrary orientation angles, dense distribution and large aspect ratios. The Oriented Bounding Box (OBB) adds different rotation angles to the horizontal bounding box, which can better deal with the above problems. However, with the introduction of oriented bounding boxes in the detector, new problems have emerged, such as the increase in the number of anchor points with different scales, angles, and aspect ratios, and the imbalance of IoU matching of positive and negative anchor boxes, which leads to classification confidence and Inconsistency between positioning accuracy. In order to overcome these shortcomings while using the oriented bounding box, this paper proposes a novel oriented target detector and redesigns a matching strategy for evaluating the position of the anchor frame. By selecting a higher quality anchor frame to achieve more accurate target detection, thereby reducing the instability of the angle in the matching process. Experiments conducted on public remote sensing data sets such as HRSC2016 and UCAS-AOD show that the proposed method achieves advanced detection accuracy.

In recent years, the automotive radar systems has gained substantial interest for different applications of autonomous driving. The performance of most applications like classification and tracking directly relies on accurate target detection. The state-of-the-art detection pipeline (i.e. algorithm) is vulnerable to multi-path reflections, clutter noise, interference from another radar and leads to false or ghost detections. To address this issue, an end-to-end target detection pipeline using a residual based U-Net architecture is proposed. In contrast to the conventional approach, the network directly generates the detection map from range-Doppler map. The network uses a generative adversarial training over multiple real world measurements. We demonstrate that the proposed network can learn effectively to detect extended targets and shows significant improvement under increased noise floor in comparison to the state-of-the-art detection techniques.

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.