Target Detection System Research Articles

IETAFusion: An illumination enhancement and target‐aware infrared and visible image fusion network for security system of smart city

AbstractIn the environmental security monitoring of smart cities, the infrared and visible image fusion method deployed on intelligent systems based on cloud and fog computing plays a vital role in providing enhanced images for target detection systems. However, the fusion quality can be significantly influenced by the illumination of the monitoring scenario in visible images. Therefore, conventional methods typically suffer a severe performance drop under the condition of insufficient illumination. To tackle this issue, we propose an illumination enhancement and target‐aware fusion method‐based on artificial intelligence, which breaks the boundaries between the task of illumination enhancement and image fusion and provide a fusion result with better visual perception in nighttime scene. Specifically, we use a light‐weight contrast enhancement module restore the brightness of the visible image. Moreover, a Swin Transformer‐based backbone network is utilized to facilitate information exchange between the source images and enhance the capabilities of target awareness. Finally, the fused images are reconstructed by the contrast‐texture retention module and reconstructor. The extensive experiments indicate that the proposed approach achieves improved performance both in human perception and quantitative analysis compared with the state‐of‐the‐art methods.

Lightweight and High-Stiffness Metal Optical Systems Based on Additive Manufacturing.

To build a long-wave infrared catadioptric optical system for deep space low-temperature target detection with a lightweight and wide field of view, this work conducted a study that encompasses a local cooling optical system, topology optimization-based metal mirror design, and additive manufacturing. First, a compact catadioptric optical system with local cooling was designed. This system features a 55 mm aperture, a 110 mm focal length, and a 4-degree by 4-degree field of view. Secondly, we applied the principles of topology optimization to design the primary mirror assembly, the secondary mirror assembly, and the connecting baffle. The third and fourth modes achieved a resonance frequency of 1213.7 Hz. Then, we manufactured the mirror assemblies using additive manufacturing and single-point diamond turning, followed by the centering assembly method to complete the optical assembly. Lastly, we conducted performance testing on the system, with the test results revealing that the modulation transfer function (MTF) curves of the optical system reached the diffraction limit across the entire field of view. Remarkably, the system's weight was reduced to a mere 96.04 g. The use of additive manufacturing proves to be an effective means of enhancing optical system performance.

Retraction Notice: Design of Target Detection and Tracking System for Sports Video

Retraction Notice: Design of Target Detection and Tracking System for Sports Video

Domestic heterogeneous FPGA-based aerial infrared target detection and tracking system

Domestic heterogeneous FPGA-based aerial infrared target detection and tracking system

Dual-Functional Microwave Photonic System for Target Detection and Frequency Measurement

Dual-Functional Microwave Photonic System for Target Detection and Frequency Measurement

ОБҐРУНТУВАННЯ СИЛ І ЗАСОБІВ НАЦІОНАЛЬНОЇ ГВАРДІЇ УКРАЇНИ, ЗАЛУЧЕНИХ ДО ВИКОНАННЯ ЗАВДАНЬ ІЗ ЗАХИСТУ КРИТИЧНОЇ ІНФРАСТРУКТУРИ ВІД ПОВІТРЯНИХ ЦІЛЕЙ

An analysis of critical infrastructure objects, which have different levels of criticality categories, and the importance of assigning individual units to destroy air targets, was carried out. The existing objects of critical infrastructure, which are subject to protection and defense by the National Guard of Ukraine in the conditions of resistance to the armed aggression of the Russian Federation, and the areas of responsibility of operational-territorial associations and military units under direct control, which carry out the protection and defense of the specified objects, were studied. The need for components of the defense system for the protection of critical infrastructure objects by military units (units) of the National Guard of Ukraine, which perform the task of destroying enemy air targets, is considered. The organization of interaction in the general air target detection system, which is connected to the network of the Air Force of the Armed Forces of Ukraine, air defense units and aims to detect enemy air targets aimed at destroying the country's critical infrastructure facilities protected by the National Guard of Ukraine, is analyzed. It is proposed to define the defense sectors of the critical infrastructure object to form the necessary type and quantity of weapons and equipment necessary for the destruction of air targets both in the approaches to the object and in the distance from it.

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

Network security intrusion target detection system in the cloud

Design of Moving Target Detection System Based on Planning Algorithm

Design of Moving Target Detection System Based on Planning Algorithm

THREE-DEGREE-OF-FREEDOM ELECTRIC MACHINE AND ITS OPERATION MODES

This paper deals with the operating modes of a 3-degree-of-freedom (3-DOF) electric machine. The design of such a machine allows the rotor axis to rotate along two angular coordinates in a limited range of angles. This is necessary for the machine to operate as part of a small-sized, high-speed precision target detection and tracking system. Based on the 3-DOF machine dynamics model, a block diagram of a servo system has been developed for controlling the rotor motion trajectory at two coordinates. Examples are given of the implementation of rotor motion trajectories with linearly increasing Examples are given of the implementation of rotor motion trajectories with linearly increasing, as well as described by an Archimedes spiral reference signals described by an Archimedes spiral reference signals. Dependencies of the modules of the relative accuracy of rotor movement along given trajectories on the system tunings were obtained. Ref. 12, fig. 9, table. Keywords: control system, motion trajectories, three-degree-of-freedom electric machine.

THE NEW ARMS RACE BETWEEN CHINA AND THE US: A COMPARATIVE ANALYSIS OF AI-POWERED MILITARY AND ECONOMIC PURSUITS

The current international system fosters change which is catalysed by a factor that is vital to any state's growth in the 21st century - the mastery of technology. To be precise, artificial intelligence (AI) constitutes the powerful leverage that countries will need to know how to use in order to gain the hegemonic advantage. It follows that the development of AI will be paramount in revolutionising the face of future, intelligentised warfare. This paper focuses on the elaboration of the AI narrative from a neorealist perspective, and its effects on the military and economic factors which will be most decisive in determining whether China will outpace the US in the years to follow. To this end, a comparative analysis will be employed, and document analysis will serve as the research method of choice. Not only will AI be essential to drive social change, but it will also be imperative in the development of military tools, such as target detection systems (TDS), and surveillance systems, both of which will serve as case studies to support the hypothesis that rapid AI developments stem from existing geopolitical tensions and competition, which drive the need for both countries to militarise. The growth potential of these technologies also prophesises a return on investment, for whichever state aims to employ them, in terms of both economic and military potential, international relevance, and, ultimately, security in the international system. As such, the important role of multinational domestic companies cannot be overstated in this case, and will be discussed.

Extended least squares (ELS) and generalized least squares (GLS) for clutter suppression in hyperspectral images: A theoretical description

Hyperspectral imaging (HSI) has been demonstrated to be useful for estimating spatial distributions of different chemical constituents on surfaces and in imaged scenes, as well as for detecting anomalies and known targets that may be present. In target detection, the objective is to find pixels that contain significant signal attributable to a known “target” spectrum. Unfortunately, measured images are often dominated by “non-target” interference signals with unknown spectra that vary image-to-image. To be effective, target detection algorithms must account for the non-target signal, commonly referred to as “clutter.” The utility of HSI for target detection in spatially complex scenarios can be attributed to two aspects associated with the sensing system: 1) The target can dominate the measured signal in a few individual pixels, although it may have only a minor contribution to the overall signal, and 2) HSI measures many pixels relevant for characterizing clutter that can be used in clutter suppression algorithms to account for interferences.Clutter suppression enables the detection of minor signal sources in HSI, resulting in a sensing system uniquely suited to highly variable environmental scenarios. Popular clutter suppression methods such as extended least squares (ELS) and generalized least squares (GLS) can be described as extensions of the classical least squares (CLS) model. The main advantage of these methods is that they are fast and easy to train on an image-to-image basis providing highly sensitive, image-specific target detection systems. However, the optimization of such models affects the sensitivity and selectivity of the detection. In an effort to assist practitioners interested in clutter suppression, this paper presents a detailed description of the theoretical aspects of ELS and GLS methods used for target detection on hyperspectral images. The concepts are demonstrated with practical examples of the detection of an acrylonitrile butadiene styrene (ABS) particle in sand based on a single near infrared hyperspectral image.

Fraud Detection under Siege: Practical Poisoning Attacks and Defense Strategies

Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this article, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle , which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: During the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.

Dual-dispersive spectral linear polarization imager based on coded-aperture

Spectral and polarization detections can increase the dimension of data acquisition and enhance the capabilities of target detection systems under complicated circumstances. A spectral linear polarization imager that combines double-disperser coded aperture imaging technology with a division of focal plane polarization detector is proposed in this paper to acquire spectral and polarization information simultaneously. First, the principles and mathematical model of the system are presented, where the proposed mathematical model considers the transformations among different coordinate systems and signal transmission processes, which is realistic and provides guidance for the calibration process. Then, targeting the detection and reconstruction methods based on the Hadamard transform, the loading approach of the coded aperture and the necessity of merging micro-mirror units are analyzed. Finally, according to the actual detection demands, the proposed system can achieve measurements and reconstructions at different spectral resolutions by loading 7th- and 15th-order cyclic S-matrix templates in real-time at the coded template. The results show the Pearson correlation coefficient and the root-mean-square error between the degree of linear polarization values acquired through proposed and reference systems are 0.9975 and 0.0238, respectively. Experimental results also demonstrate that although 15th-order cyclic S-matrix template can result a higher spectral resolution compared with 7th-order cyclic S-matrix template, it decreases the accuracy of the reconstructed results, indicating a trade-off between spectral resolution and accuracy in actual detections.

ResFuseYOLOv4_Tiny: Enhancing detection accuracy for lightweight networks in infrared small object detection tasks

When deploying infrared target detection systems at the edge, one often encounters constraints on computational and storage resources. In such scenarios, lightweight networks prove to be a viable detection solution. Compared to large-scale deep networks, lightweight networks, after discarding redundant structures, often do not achieve satisfactory detection accuracy, especially in the context of small object detection tasks. This paper proposes ResFuseYOLOv4_Tiny, an improved lightweight network specifically designed for the effective detection of infrared small objects, particularly in resource-constrained scenarios. We extend the YOLOv4_Tiny neck network to enable efficient feature extraction. By integrating Res_head with residual structures and fusion-based feature reuse, our framework substantially enhances the capability to detect small objects by expanding the receptive field and effectively merging features from various receptive fields. Additionally, we introduce a drop and adaptive parameter-based attention mechanism called DCA, leveraging the inclusion of drop layers to effectively reduce information redundancy. Moreover, we replace the conventional CIoU loss function with our customized MSIoU, incorporating parameter m to adaptively adjust the loss weights in response to the influence of small target sizes. Experimental results conducted on synthetic and real infrared small object datasets respectively reveal a significant 14.47% and 11.28% improvement in detection accuracy. The proposed ResFuseYOLOv4_Tiny framework presents a viable solution for lightweight networks when both detection speed and accuracy are concurrently considered.

Availability evaluation model for space-based optical aerial target detection system

Availability evaluation model for space-based optical aerial target detection system

Target recognition and detection system based on sensor and nonlinear machine vision fusion

Abstract In order to realize the automatic detection system of electric sensor, a method based on sensor and nonlinear machine vision is proposed. Aiming at complex scenes and dynamic changes in target recognition and detection in large-scale industrial field, a target recognition and detection system based on the fusion of vision sensor and nonlinear machine vision is proposed. The system introduces nonlinear features and uses deep neural network to realize multi-scale analysis and recognition of image data on the basis of traditional machine vision. The system uses C++ language development and has a good user interface. The photoelectric sensor weld image is collected by machine vision technology, the target area of the image is detected by Gaussian model, the feature points of the target area are extracted by combining Hessian matrix, the extracted feature points are input into the quantum gate neural network model, and the recognition results are obtained. The simulation results show that the author’s method has the highest value among the three test indicators, with the highest accuracy rate of 97%, the highest recall rate of 98%, and the highest F1 value of 94. The time consumed by the author’s method for automatic identification of photoelectric sensor welding is within 6 s, the time spent on the film wall recognition method for automatic identification of photoelectric sensor welding is within 20 s, and the time spent by the feature extraction and identification method for automatic identification of photoelectric sensor weld is within 22 s. It has been proven that the method based on the fusion of sensors and nonlinear machine vision can achieve an automatic recognition and detection system for electrical sensor welds. The object detection and recognition method proposed in this article can be applied to dynamic changes and complex scenes in various complex backgrounds and has a good application prospect. The system proposed in this article has some limitations, such as the algorithm in the calculation accuracy, real-time, and other aspects that have room for improvement.

Measurement of the neutron energy response curve of 4H-SiC detector based fission target detection system at the CSNS Back-n white neutron source

The fission target detection system, which is based on U-235(or U-238)fission target and large area 4H-SiC detector, has been developed for pulsed neutron detection. However, calibration of its neutron energy response curve is challenging due to its very low detection efficiency to fast neutrons. In the past, the calibration of the neutron sensitivity for the fission target detection system was limited to using ∼ 14 MeV neutrons generated by the Cockcroft-Walton accelerator. To obtain the response function for the complete range of neutron energies, extrapolation based on neutron fission cross sections was required. In this paper, we present the calibration of its energy response at the China Spallation Neutron Source (CSNS) Back-n beam line using the time of flight method, both with double-bunch and single-bunch mode in end-station 1(with a flight path of ∼ 55 m). The SiC detector with a thin sensitive thickness of 30 μm has lower scattering background than the traditional Si-PIN detector . As a result, the energy response curve of the fission target detection system above 0.26 MeV has been successfully acquired. The spectrum of the double-bunch mode was unfolded using the Bayesian algorithm, and the results indicate that, under conditions of sufficient counts, this method can yield results similar to those obtained from the single-bunch mode. The issue of pulse height defect of SiC detector to fission fragments is discussed. This experiment confirms that our detection system of SiC+fission target has a strong signal-to-noise ratio and anti-interference capability. At the same time, it demonstrates that this device has its own advantages in measuring the energy response of low sensitivity detectors.

Miniaturized customized filtering-wheel-based multispectral imaging system for target detection

Filtering-wheel-based multispectral imaging (FW-MSI) systems are utilized in various applications. However, the inherent drawbacks of existing FW-MSI systems, such as traditional motors with large volumes and the configuration of filters, pose challenges for miniaturization. Therefore, we propose a miniaturized customized filtering-wheel-based multispectral imaging (MCFW-MSI) system. Our own-developed PCB-based small motor, a new layout of the filter and the least number of filtering channels offer the main design priority for our system. Specifically, a new layout of the integrated multi-channel filter combining the developed motor is constructed, making the filtering module extremely compact. Furthermore, the least number of filtering channels is guaranteed through band selection, further ensuring miniaturization and customization. Finally, the MCFW-MSI system is obtained with the volume of Φ38mm × 23 mm and weight of ∼ 98 g for recognizing command post and camouflage net, for instance, to verify its detection capability. Our system supports modular functionality to accommodate different scenarios.

Retracted: Application of Embedded Intelligent Target Detection System in Basketball Movement Test

Retracted: Application of Embedded Intelligent Target Detection System in Basketball Movement Test