Military Target Detection Research Articles

Synthesis of Chlorophylls-Doped Guanine Crystals with High Reflection and Depolarization for Green Camouflage Coating.

Hyperspectral imaging technology can record the spatial and spectral information of the targets and significantly enhance the levels of military reconnaissance and target detection. It has scientific importance to mimic "homochromatic and homospectral" camouflage materials that have hyperspectral similarity with the green vegetation, one of the most common natural backgrounds. It is a big challenge to exquisitely simulate the spectral of green vegetation in visible and near-infrared windows because of the slight differences between the artificial green dyes and vegetation, the instability of chlorophylls, and the easy loss of hydroxide bands due to the loss of water from the camouflage materials. Herein, a novel kind of biomimetic material of green vegetation was designed through the incorporation of chlorophylls into the crystal lattices of single-crystalline anhydrous guanine microplates for the first time. The synthesized chlorophylls-doped anhydrous guanine crystals exhibit high reflectance intensity and depolarization effect, thus can be applied as biomimetic camouflage materials that mimic green vegetation with high reflectivity and low polarization in the visible and near-infrared regions. The factors influencing the formation of dye-doped organic crystals under mild conditions were thoroughly investigated and the characterizations using electron microscopies and fluorescence confocal laser scanning microscopy clearly confirm the occlusion of chlorophylls into the crystal lattices of guanine crystals. The thermal stability experiments clearly indicate that the chlorophylls-doped guanine crystals possess long-term stability at high temperature. This study provides a new strategy for the synthesis of multifunctional materials comprised of organic crystals.

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.

Military target detection method based on EfficientDet and Generative Adversarial Network

Military target identification is one of the first tasks of modern counter-terrorism operations, and military target detection methods based on unmanned system platforms can effectively reduce personnel casualties and improve combat effectiveness. Due to the complexity and variability of the actual combat environment and the demand for real-time target recognition, this paper proposes an image recognition method based on the combination of EfficientDet and Generative Adversarial Network (GAN), in which the gauged image features extracted from the EfficientDet model are used as the input of the GAN for the game learning of image categories and features. The learning results are also used as the feature reuse input of the EfficientDet for feature learning, so that this recognition model can obtain higher recognition speed and recognition accuracy. Through model testing with embedded experiments on unmanned platforms, the results show that the network has a higher mean-Average-Precision compared to the traditional one-stage approach, while improving the recognition accuracy of targets in different complex environments while maintaining no significant reduction in Frames Per Second.

A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board

A large number of emerging applications, such as autonomous navigation, space exploration, surveillance, military target detection, and remote sensing, use outdoor images to monitor various activities of interest. However, images acquired under unfavorable weather conditions usually suffer from atmospheric scattering due to environmental pollution causing color-shift and low-contrast images. Dehazing is an emerging research area in the computer vision domain that intends to restore the visibility of images by eliminating the latter types of degradation. Single image dehazing, on the other hand, is more challenging since it necessitates a precise assessment of atmospheric light and transmission map. This study aims to design a dual-channel deep neural network (DCD-Net) for estimating the transmission map, further utilized to compute atmospheric light. Finally, a dehazed image is generated using the obtained atmospheric light and the transmission map. The experimental results are compared qualitatively and quantitatively with eight existing dehazing approaches based on ten metrics on six publicly available standard datasets: Foggy Road Image DAtabase, HazeRD, REalistic Single Image DEhazing, NYU-Depth, O-HAZE, I-HAZE, a few natural hazy images, and underwater images. The DCD-Net outperforms conventional techniques, according to extensive studies. Moreover, a range of relative improvements of the proposed method over other approaches is calculated for better analysis of the results. A visual internet of things (VIoT) framework employing a PYNQ-Z2 board is presented in addition to the DCD-Net. It can be applied in real-time applications, particularly in the transportation and surveillance industries. The DCD-Net is suitable for image dehazing by virtue of its multilayered structure. The VIoT uses the DCD-Net for dehazing, while the PYNQ-Z2 board serves as the central processing unit. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problems occurring due to haze. Haze reduces the visibility of a scene, causing major concerns in transportation and surveillance. Existing approaches have attempted to address this issue, albeit the methods are limited. As a result, this study proposes a new dual-channel CNN model with two modules, where the first module calculates fine details of the image and the second module estimates the transmission map. Furthermore, both features are combined to produce a more reliable transmission map. The training process highly influences the resulting output of the network. Therefore, an algorithm explaining the training instructions for the practitioners is given in the appendix. The obtained transmission map is further used to estimate atmospheric light. The images are then dehazed using atmospheric light and transmission maps. In addition, we have designed a framework for image dehazing using VIoT with a PYNQ-Z2 board. Experimental results suggest that this approach gives expected results, yet, there is one limitation. This method requires a haze image and a corresponding transmission map, which is not always possible. Therefore, we will attempt to design a semi-supervised learning approach in the future.

Real-time Military Target Detection and Recognition in Complex Environments with Mask R-CNN

Real-time Military Target Detection and Recognition in Complex Environments with Mask R-CNN

Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation

The precise detection of military targets under complex conditions is a key factor to enhance the ability of war situation generation and prediction. The current technology can not overcome the problems of smoke and occlusion interference, target height change, and different scales in aerial video. In this paper, a multi feature cross fusion and cross layer cascade aerial military target detection algorithm (YOLOv5-MFLC) is proposed. Firstly, aiming at the high confidentiality of the military targets and the shortage of battlefield aerial image resources, a real scene based aerial military target dataset is constructed, and the methods of random splicing and random extraction embedding are used for data enhancement in order to improve the diversity and generalization of targets. Secondly, aiming at the problem of complex background interference, a multi feature cross fusion attention mechanism is constructed to enhance the available information of target features. Finally, for the multi-scale problem of targets in aerial images, a cross layer cascaded multi-scale feature fusion pyramid is designed to improve the detection accuracy of cross scale targets. The experimental results show that, comparing with the existing advanced detection models, the detection accuracy of the algorithm in this paper has been greatly improved. The average accuracy of the algorithm can reach 81.0%, which is 5.2% higher than the original network. In particular, it has reached 55.9% in the smaller target category "person", which is 9.4% higher. And the experimental results further show the usefulness of the improved algorithm for small target detection. At the same time, the detection rate of this algorithm can reach 56 frame/s, which can effectively achieve accurate and fast detection of battlefield targets, and has certain experience value for guiding complex modern wars.

A generalized control chart for anomaly detection in SAR imagery

Synthetic aperture radar (SAR) imagery is widely used in Earth observation (EO) applications such as land-use change monitoring and target detection. For such applications, it is important to have accurate knowledge of the statistical properties of SAR images to make good use of the information available. Therefore, it is essential to have appropriate statistical distributions to model the SAR data. In this context, the goal of this paper is threefold. First, a new parameterization of the Burr XII distribution that generalizes some widely used distributions for modeling SAR data is proposed. Second, based on the reparameterized distribution, a generalized control chart for anomaly detection in digital images is introduced. Using Monte Carlo simulations, the power detection of the proposed control chart by the empirical run length distribution is evaluated. Finally, the proposed methodology is applied to a real problem of anomaly detection. The problem of interest is the detection of military targets concealed in a forest region. The results suggest the relevance of the proposal to detect anomalies in SAR imagery, evidencing its practical applicability in EO problems.

Multi-Scale Infrared Military Target Detection Based on 3X-FPN Feature Fusion Network

Multi-Scale Infrared Military Target Detection Based on 3X-FPN Feature Fusion Network

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

Hyperspectral imaging can simultaneously obtain the spatial morphological information of the ground objects and the fine spectral information of each pixel. Through the quantitative analysis of the spectral characteristics of objects, it can complete the task of classification and recognition of ground objects. The appearance of imaging spectrum technology provides great advantages for military target detection and promotes the continuous improvement of military reconnaissance levels. At the same time, spectral camouflage materials and methods that are relatively resistant to hyperspectral reconnaissance technology are also developing rapidly. In order to study the reconnaissance effect of visible/near-infrared hyperspectral images on camouflage targets, this paper analyzes the spectral characteristics of different camouflage targets using the hyperspectral images obtained in the visible and near-infrared bands under natural conditions. Two groups of experiments were carried out. The first group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage clothing with grassland as the background; the second group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage paint sprayed on boards and steel plates. The experiment shows that the hyperspectral image based on the near-infrared band has a good reconnaissance effect for different camouflage targets, and the near-infrared band is an effective “window” band for detecting and distinguishing true and false targets. However, the stability of the visible/near-infrared band detection for the target identification under camouflage paint is poor, and it is difficult to effectively distinguish the object materials under the same camouflage paint. This research confirms the application ability of detection based on the visible/near-infrared band, and points out the direction for the development of imaging detectors and camouflage materials in the future.

A Lightweight Military Target Detection Algorithm Based on Improved YOLOv5

Military target detection technology is the basis and key for reconnaissance and command decision-making, as well as the premise of target tracking. Current military target detection algorithms involve many parameters and calculations, prohibiting deployment on the weapon equipment platform with limited hardware resources. Given the above problems, this paper proposes a lightweight military target detection method entitled SMCA-α-YOLOv5. Specifically, first, the Focus module is replaced with the Stem block to improve the feature expression ability of the shallow network. Next, we redesign the backbone network of YOLOv5 by embedding the coordinate attention module based on the MobileNetV3 block, reducing the network parameter cardinality and computations, thus improving the model’s average detection accuracy. Finally, we propose a power parameter loss that combines the optimizations of the EIOU loss and Focal loss, improving further the detection accuracy and convergence speed. According to the experimental findings, when applied to the self-created military target data set, the developed method achieves an average precision of 98.4% and a detection speed of 47.6 Frames Per Second (FPS). Compared with the SSD, Faster-RCNN, YOLOv3, YOLOv4, and YOLOv5 algorithms, the mAP values of the improved algorithm surpass the competitor methods by 8.3%, 9.9%, 2.1%, 1.6%, and 1.9%, respectively. Compared with the YOLOv5 algorithm, the parameter cardinality and computational burden are decreased by 85.7% and 95.6%, respectively, meeting mobile devices’ military target detection requirements.

Military Target Detection in Remote Sensing Imagery Based on YOLOv4-Faster

The continuing development of remote sensing has resulted in a rapidly increasing number of remote sensing applications. High-resolution remote sensing images are used in various fields in the military. We propose methods for object detection based on remote sensing images. We develop a signal processing method for normalizing remote sensing images to eliminate noise such as fog, haze, and poor lighting. This method further improves detection accuracy and reduces error rates. We develop YOLOv4-faster, an accelerated neural network model based on the YOLO (You-Only-Look-Once) object detection method. YOLOv4-faster outperforms existing networks in terms of execution time and detection performance. We conduct a series of&#xA0;experiments on two public datasets (TGRS-HRRSD and NWPU VHR-10) as well as a dataset containing six military target classes provided by IMINT &amp; Analysis and collected from Google Earth. YOLOv4-faster improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.

YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection

Military target detection technology is the foundation and key to perceive and analyze the battlefield situation, and it is also the premise of target tracking technology. Aiming at the task of military target detection, the detection performance of traditional detection algorithms is poor in complex environment. We realize automatic detection of military targets in complex environment through deep learning. In this research, we improve the components of YOLOv3 and propose a novel military target detection algorithm (YOLO-G). We have built a military target dataset composed of armed men with different weapons, which provides a test environment for various object detection algorithms. In the YOLOv3 network structure, by introducing the lightweight convolutional neural network GhostNet as the feature extraction network, the accuracy and speed of military target detection are improved. Then, the attention mechanism based on the coordinate attention block is introduced to enhance the representation ability of target features, suppress interference and improve the detection accuracy. Finally, the loss function of the target detector is redesigned by using DIOU loss function and Focal loss function, which further improves the detection accuracy of our detection model for military targets. We tested YOLO-G on the military target dataset. Experimental results show that our method improves the mAP by 2.9% and the detection speed by 25.9 frames/s compared with the original YOLOv3 algorithm, and the size of the proposed model is reduced to 1/6 of that of YOLOv3. In addition, we also compare our method with state-of-the-art object detection algorithms. The results show that YOLO-G also has superior detection performance, and the mAP index obtained by our method is 1.2% higher than that of the latest YOLOv5 on the premise of meeting the application requirements, which plays an important auxiliary role in the generation and analysis of battlefield situation.

Military Target Detection Method Based on Improved YOLOv3 Network

Military Target Detection Method Based on Improved YOLOv3 Network

Rapid Detection of Camouflaged Artificial Target Based on Polarization Imaging and Deep Learning

Polarization imaging has the advantage of detecting artificial targets based on their intrinsic characteristics. However, with the development of camouflage materials and camouflage shielding performance, the anti-optical detection technology for camouflaged targets continues to improve. In this paper, we combine the advantages of polarization imaging and deep learning to achieve rapid detection of artificial targets camouflaged in natural scenes. Firstly, we propose a Stokes-vector-based parameter image to show the polarization specificity of the camouflaged artificial targets. Then, a detection method is proposed, which uses an Otsu segmentation algorithm and morphological operations to extract polarization signatures of the target from the proposed parameter image, and utilizes the extracted polarization signatures to highlight the camouflaged artificial targets. Finally, we improve a self-supervised deep learning network to enhance the low-light images, extending the application of our method into low illumination environment target detection. Experimental results demonstrate that our method can effectively detect the camouflaged artificial targets with a detection rate better than 80%, which has potential application value in the fields of military target detection, security monitoring, and remote sensing.

Inter/intra-category discriminative features for aerial image classification: A quality-aware selection model

Classification, recognition, and quality assessment of aerial images strongly depends on detecting and identifying their discriminative visual features. In practice, aerial images provide clues for various applications, including disaster prediction, automatic navigation, and military target detection. However, the detection of discriminative cues in aerial images is quite problematic since the aerial image quality is susceptible to luminance and noise, while aerial images have significantly different topological structures. We propose a novel method to explore quality-related and topological cues from aerial images for visual classification to mitigate these problems. We first decompose aerial images into several components, each being processed via the morphological filtering. Subsequently, we leverage the quality model to generate discriminative regions and topologies. Each aerial image is represented using a feature vector extracted from these regions. Afterward, we train a CNN-based visual classification model to predict aerial image categories. Experimental results have shown that our method can effectively predict aerial image categories, and the proposed algorithm is more robust than other state-of-the-art ones.

Multilayer Feature Extraction Network for Military Ship Detection From High-Resolution Optical Remote Sensing Images

Rapid and accurate detection of maritime military targets is of great significance for maintaining national defense security. Few studies have used high-resolution optical images for the detailed classification of maritime military targets. This article, inspired by EfficientDet trackers, presents a method to classify military targets on the sea from high-resolution optical remote sensing images. In the first stage, a multilayer feature extraction network is constructed to extract various features. At the same time, residual connection and dilation convolution are introduced to prevent the deep network features from disappearing. Moreover, we use multilevel attention mechanism approaches to make more effective use of multilayer features. ReLU is introduced to replace the original swish activation function to reduce the computational cost in the pretreatment stage. After this, deep feature fusion networks and prediction networks are constructed to locate and distinguish different types of ships. Different types of ships use different degrees of data expansion methods to solve the problem of sample shortage and imbalance. The multiclassification method is used to solve low classification accuracy caused by little difference between civil and military ships. Experimental results suggested that the proposed method can accurately identify multiple types of military ships.

RETRACTED ARTICLE: Content based satellite image retrieval system using fuzzy clustering

Nowadays, Satellite image retrieval could be a huge issue to induce information for natural disaster management, military target detection, meteorology, urban designing, harm assessment and change detection, etc. Basis on the image substance, content based image retrieval extracts the images that are relevant to the user given query image from massive image databases. Most of the existing image retrieval methods are still incompetent of providing retrieval outcome with elevated retrieval accuracy and not as much of computational intricacy. This paper proposed Fuzzy multi-characteristic clustering technique to realize this goal that is based on Fuzzy logic and clustering. Fuzzy sets used to represent the vagueness occur in user query, similarity measure and image substance. Clustering is an unsupervised method of classification that provides a small amount of control to clustering and improves the clustering performance drastically. The tentative results reveal that our proposed method can achieve significant precision and recall rates with better computational efficiency.

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

Hot region selection (HRS) is usually performed for larger scenes, especially in military target detection and battlefield situational awareness applications. Many state-of-the-art methods pay more attention on object detection. Although they can detect the region of interest in one image, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. The study of HRS is of great importance in the analysis of remote sensing images. The current research focuses on the specific type of object area detection. A well-developed HRS needs to have three properties: uniform highlighting of the entire HRS, well-defined boundaries, and good robustness. Motivated by these requirements, a HRS method based on the selective search method and modified fuzzy c-means (FCM) in remote sensing images is proposed to address the detection of potential hot regions in large-scale remote sensing images. First, we create a Gaussian curvature filter to preprocess large scale remote sensing images. Second, a modified FCM segmentation method is utilized to segment the image. Third, an enhanced selective search method is adopted to establish well-defined boundaries for the HRS and to improve the immunity to noise. The geographic information is presented in this phase, which is conducive to improve the detection accuracy. In the experimental section, we compare our new method with four other extraction models on three data sets. The experimental results show that compared to the other competing models, the new model better defines the hot regions and obtains more entire boundaries in terms of Overlap and mA P.

Hyperspectral image quality based on convolutional network of multi-scale depth

Hyperspectral imagery has been widely used in military and civilian research fields such as crop yield estimation, mineral exploration, and military target detection. However, for the limited imaging equipment and the complex imaging environment of hyperspectral images, the spatial resolution of hyperspectral images is still relatively low, which limits the application of hyperspectral images. So, studying the data characteristics of hyperspectral images deeply and improving the spatial resolution of hyperspectral images is an important prerequisite for accurate interpretation and wide application of hyperspectral images. The purpose of this paper is to deal with super-resolution of the hyperspectral image quickly and accurately, and maintain the spectral characteristics of the hyperspectral image, makes the spectral separability of the substrate in the original image remains unchanged after super-resolution processing. This paper first learns the mapping relationship between the spectral difference of low-resolution hyperspectral image and the spectral difference of the corresponding high-resolution hyperspectral image based on multiple scale convolutional neural network, Thus, apply this mapping relationship to the input low-resolution hyperspectral image generally, getting the corresponding high resolution spectral difference. Constrained space by using the image of reconstructed spectral difference, this requires the low-resolution hyperspectral image generated by the reconstructed image is to be close to the input low-resolution hyperspectral image in space, so that the whole process becomes a closed circulation system where the low-resolution hyperspectral image generation of high-resolution hyperspectral images, then back to low-resolution hyperspectral images. This innovative design further enhances the super-resolution performance of the algorithm. The experimental results show that the hyperspectral image super-resolution method based on convolutional neural network improves the input image spatial information, and the super-resolution performance of the model is above 90%, which can maintain the spectral information well.

Retrieval of Ionospheric Faraday Rotation Angle in Low-Frequency Polarimetric SAR Data

A low-frequency spaceborne synthetic aperture radar (SAR) working system, e.g., operating at the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> -band or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -band, has great advantages of military target detection and biomass monitoring. Nevertheless, it is more susceptible to ionospheric effects compared with the higher frequency system. A trans-ionospheric wave propagation model is established in this paper to incorporate ionospheric effects on SAR signals. As one of the significant distortion sources for the polarimetric SAR (PolSAR), Faraday rotation (FR) is mainly imposed by background ionosphere, and its spatial variation is discussed. FR estimators have been devised in succession to estimate FR angle (FRA), and various potential novel estimators can still be derived. But, from a viewpoint of theoretical expressions, the earliest estimator is bound to be the optimal one. Based on PolSAR real data, this mathematical conclusion is further validated via comprehensive performance analysis as to estimation bias and standard deviation rather than the existent root-mean-square principle. Finally, a step-by-step procedure of the FRA map is proposed and operated with an application of the airborne P-band PolSAR data. In particular, the ambiguity error of FRA estimates within a SAR observation is simulated and resolved. By processing the ALOS-2 real data, the spatial distribution of FRAs is retrieved and used to operate ionospheric total electron content soundings.