Target Detection Applications Research Articles

Advanced Scattering Analysis of Targets Under Plane Waves and Electromagnetic Vortex Waves for Stealth Applications

This study investigates the scattering characteristics of typical metallic targets under the influence of electromagnetic vortex waves, with a focus on Radar Cross Section (RCS) and Orbital Angular Momentum Radar Cross Section (ORCS). Using the physical optics approximation, the induced current density on the target surface was calculated, and the backscattered field was derived. The comparative analysis highlights the advantages of electromagnetic vortex waves over plane waves in stealth applications, with an in-depth examination of the impact of different OAM modes on power distribution and flat plate scattering characteristics. The unique properties of electromagnetic vortex waves show promising applications in stealth radar and target detection. Furthermore, the study discusses the potential for using OAM waves in enhancing radar system performance, target identification, and electronic countermeasures. The ability of OAM waves to provide selective detection capabilities and reduce RCS under certain conditions makes them particularly valuable for military stealth applications. The combination of MIMO technology with OAM waves is also explored as a means to further improve detection capabilities and flexibility. The findings suggest that electromagnetic vortex waves have a broad range of applications beyond stealth, including radar sensing, imaging, and rotational Doppler detection, thereby opening new avenues for advanced radar technologies

Research on Object Detection in Complex Scenarios Based on ASA‐YOLOv5

The applications of target detection in complex scenarios cover a wide range of fields, such as pedestrian and vehicle detection in self‐driving cars, face recognition and abnormal behavior detection in security monitoring systems, hazardous materials safety detection in public transportation, and so on. These applications demonstrate the importance and the prospect of wide application of target detection techniques in solving practical problems in complex scenarios. However, in these real scenes, there are often problems such as mutual occlusion and scale change. Therefore, how to accurately identify the target in the real complex scenarios has become a big problem to be solved. In order to solve the above problem, the paper proposes a novel algorithm, Adaptive Self‐Attention‐YOLOv5 (ASA‐YOLOv5), which is built upon the YOLOv5s algorithm and demonstrates effectiveness for target identification in complex scenarios. First, the paper implements a fusion mechanism between the trunk and neck networks, enabling the fusion of features across different levels through upsampling and downsampling. This fusion process mitigates detection errors caused by feature loss. Second, the Shuffle Attention mechanism is introduced before upsampling and downsampling to suppress noise and amplify essential semantic information, further enhancing target identification accuracy. Lastly, the Adaptively Spatial Feature Fusion (ASFF) module and Receptive Field Blocks (RFBs) module are added in the head network, and it can improve feature scale invariance and expand the receptive field. The ability of the model to detect the target in the complex scene is improved effectively. Experimental results indicate a notable improvement in the model's mean Average Precision (mAP) by 2.1% on the COCO dataset and 0.7% on the SIXray dataset. The proposed ASA‐YOLOv5 algorithm can enhance the effectiveness for target detection in complex scenarios, and it can be widely used in real‐world settings. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

An intelligence enhancement method for USV navigation visual measurement based on variable gradient soft-threshold correction

Maritime image enhancement is crucial in marine engineering for applications like unmanned surface vessels (USV) visual measurement and path planning. This paper proposes an image enhancement algorithm based on variable gradient soft-threshold correction (VGSC), tailored for USV intelligent navigation. By calculating the gradient of the two-dimensional difference quotient function of the pixels, we determine the pixel distribution and threshold. An innovative dynamic equalization function is applied to compress maximum pixel values and stretch minimum ones, allowing for adaptive adjustment of the threshold. The results indicate that the VGSC algorithm effectively suppresses background noise, thereby facilitating the extraction of target contour pixel information. When evaluated using four image assessment indicators, the VGSC algorithm enhances the image quality by 20.09 % and 28.92 % compared to bilateral filtering and Gamma correction, respectively. This method excels in enhancing image details and suppressing noise, promising robust application in multi-modal data fusion and precise maritime target detection.

Highly responsive cryogel based sensing platform by encapsulating programmed DNA for colorimetric detection of 17β-estradiol

Responsive hydrogels show obvious superiorities when being employed at detection owing to their polymeric networks and optical properties. Albeit important, the slow response of traditional hydrogels against targets limits their further applications, and enhancing the responsiveness of hydrogel is therefore imperative. Herein, a cryogel with hierarchical structures was designed through adjusting the hydrogel structure by the freezing process to improve the responsive properties, and fabricated for rapid analysis of 17β-estradiol (E2). The cryogel encapsulating the DNA hairpins rapidly adsorbed the complementary DNA isolated from the aptamer in the presence of E2 and activated an efficient hybridization chain reaction (HCR). After integration with a smartphone system, a portable colorimetric sensing platform was created for sensitive detection of E2 with a low detection limit of 0.5 nM. Our work provides a promising way for highly responsive hydrogel-based biosensor design, and broadens their applications in rapid and portable target detection.

Application of Target Detection Based on Deep Learning in Intelligent Mineral Identification

In contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object detection model for intelligent mineral identification, specifically employing the YOLOv8 algorithm. The model was developed with a focus on seven common minerals: biotite, quartz, chalcocite, silicon malachite, malachite, white mica, and pyrite. During the training phase, the model learned to accurately recognize and classify these minerals by analyzing and annotating a large dataset of mineral images. After 258 rounds of training, a stable model was obtained with high performance on key indicators such as Precision, Recall, mAP50, and mAP50–95, with values stable at 0.91766, 0.89827, 0.94300, and 0.91696, respectively. In the testing phase, using samples provided by the Geological and Mineral Museum at the School of Earth Sciences and Engineering, Sun Yat-sen University, the model successfully identified all test samples, with 83% of them having a confidence level exceeding 87%. Despite some potential misclassifications, the results of this study contribute valuable insights and practical experience to the development of intelligent mineral recognition technologies.

Spreading Sea Clutter Suppression for High-Frequency Hybrid Sky-Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved.

Reconstruction algorithm of Gm-APD LiDAR based on synchronized pseudo-random time coding

Efficient photon counting imaging of long-range targets is challenging, especially in low signal-to-back ratio (SBR) situations. In this paper, a synchronized pseudo-random time coding detection method for Geiger-mode avalanche photodiode (Gm-APD) light detection and ranging (LiDAR) is proposed, which realizes Gm-APD LiDAR long-range detection by combining pseudo-random time coding and time of flight, and the multiple peaks are used to de-noise the image at the image level. Scenes of building with different SBR are simulated with Monte Carlo methods, and the effect of coding length on algorithm performance is discussed and compared with the performance of non-coding algorithms. The high performance of the proposed algorithm is verified by field experiments in low SBR scenes. Moreover, the proposed algorithm improves the target reconstruction correctness ratio by 70% over the other non-coding algorithms to 83% in the scene of building with SBR of 0.015, and has the smallest root mean square error (RMSE) of the distance. It improves the target integrity and provides the possibility of practical application of LiDAR target detection in very low SBR and long-range conditions, and even more support for target feature extraction and target recognition.

Research on high energy efficiency and low bit-width floating-point type data for abnormal object detection of transmission lines

Achieving a balance between accuracy and efficiency in target detection applications is an important research topic. To detect abnormal targets on power transmission lines at the power edge, this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization. By performing exponent prealignment and mantissa shifting operations, this method avoids the frequent alignment operations of standard floating-point data, thereby further reducing the exponent and mantissa bit width input into the training process. This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy. Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines. The results indicate that while maintaining accuracy at a basic level, the proposed method can significantly reduce the data bit width compared with single-precision data. This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits. Furthermore, a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability.

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

IntroductionSea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.MethodsTo address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.Results and discussionThe significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

Research on SAR image denoising method based on feature extraction

AbstractDue to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non‐local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background.

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

In response to the challenges associated with the misassembly, omission, and low manual inspection efficiency in automobile wiring harness relay assemblies, a novel online detection system has been engineered. This system consists of a mobile-based visual imaging system and an improved YOLOv5-based detection algorithm that tracks human movement to acquire images and videos. The system is coupled with deep learning for real-time detection and recognition for error-proofing the installation process of automotive wiring harness relays. This innovation aims to facilitate error-proof inspection during the assembly process of automotive wiring harness relays. The YOLOv5s model is augmented with an Adaptive Spatial Feature Fusion (ASFF) module, enhancing multi-scale feature integration capabilities. A Global Context Network (GCNet) is incorporated into the C3 module to emphasize target information from a global perspective. Additionally, the replacement of standard Convolution (Conv) modules with Global Sparse Convolution (GSConv) modules in the Neck section effectively reduces computational costs while sustaining overall performance efficacy. The experimental results show that the detection system achieved a comprehensive accuracy rate of 99.2% and an F1 score of 99.29. The system possesses high accuracy and stability, enabling flexible and intelligent target detection applications in the automotive industry.

Depolarization of metal surfaces based on Mueller and integral equation method

This paper delves into the depolarization phenomenon of materials using the integral equation method and Mueller matrix method. In the integral equation method, it is observed that depolarization trends with roughness are similar at different wavelengths, but numerical differences exist. The results are well-supported by both theory and experiments. Specifically, at 1064 nm wavelength, materials exhibit smaller depolarization peaks, smoother trends, and right-shifted peaks compared to 633 nm. Additionally, the polarization characteristics of materials may change with varying incident polarization states. The Mueller matrix method investigates the depolarization trend with varying incident angles and different roughness levels. It reveals a gradual increase in depolarization with the incident angle until 60°, followed by a rapid rise, reaching a peak around 80°. Moreover, materials with higher absorption coefficients exhibit stronger depolarization effects. Overall, this research uncovers the impact of absorption and scattering on the polarization properties of materials, providing valuable insights for future studies in polarization recognition.

End-to-end Remote Sensing Image Aircraft Target Detection and Fine-grained Recognition Framework

In view of the practical application of target detection and recognition tasks for remote sensing, an end-to-end aircraft target detection and fine-grained recognition framework is proposed. It can accurately and quickly implement detection and recognition in an end-to-end way. The main network of the framework adopts the design ideas of target detection and fine-grained recognition methods using candidate region extract and visual attention, making sure the accuracy of detection and recognition. Then, to solve the problem of high false detection rate and missed detection rate of densely arranged targets, we propose the re-detection mechanism. To minimize the large amounts of calculations of deep networks and improve real-time performance, we introduce depthwise separable convolution to optimize networks. Finally, a weight mapping idea based on transfer learning is adopted to solve the problem of data labeling and also helps the detection and fine-grained recognition. The results prove that the proposed framework has good robustness, versatility, and efficiency in aircraft detection and fine-grained recognition tasks.

Space-Based THz Radar Fly-Around Imaging Simulation for Space Targets Based on Improved Path Tracing

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.

High-efficiency broadband achromatic metalenses for visible full-stokes polarization imaging.

Polarization-imaging technology has important applications in target detection, communication, biomedicine, and other fields. A polarization imaging system based on metalenses, which provides new possibilities for the realization of highly integrated full-Stokes polarization imaging systems, can solve the problems of traditional polarization imaging systems, such as complex structures, large volumes, and the inability to simultaneously obtain linear and circular polarization states. However, currently designed metalens arrays that can achieve real-time full-Stokes polarization imaging can generally only be used for monochromatic detection, which significantly limits the amount of measured information of the object. Broad-spectrum polarization color imaging allows more image degrees of freedom, enabling more accurate characterization of polarization for multi-target object scenes in complex environments. To achieve broad-spectrum polarization imaging, we propose and design a metalens array that can achieve full-Stokes polarization imaging in the broadband visible range, in which the design process of metalenses for splitting and focusing broadband orthogonal circularly polarized light is emphasized. To design metalenses that can achieve polarization splitting and efficient focusing, we simulate and optimize the height and period of the nano-units and show that smaller periods and larger heights do not always result in higher-performance devices when designing multifunctional metalenses. The designed metalens array can split and diffraction-limited focus the orthogonal polarized incident light to the designated position with average focusing efficiencies of 59.2% under 460-680 nm TM linearly polarized light, 53.1% under TE linearly polarized light, 58.8% under left-handed circularly polarized light, and 52.7% under right-handed circularly polarized light. The designed metalenses can be applied to imaging systems, such as polarization imaging and polarization light-field imaging systems.

Short Distance Detection and Control System Based on Photoelectric Sensor and Its Application in Target Detection of Handling Robot

The photoelectric sensor is an electronic device that is widely used in industrial automation processes. The device uses light to detect whether an object exists. The photoelectric sensor detection control system is the core part of the photoelectric sensor. The system includes sensor processing chip, control circuit, detection optical structure and automatic teaching control algorithm. In this research, the core control chip of the automatic teaching system and the core part of the control circuit of the short-distance photoelectric sensor are set up. The modular circuit around the above chip is designed. The optical structure of the sensor is designed by Position sensitive sensor (PSD) photoelectric accessories and triangulation method. Meanwhile, the core circuit control chip (CSP) is applied to complete the basic functions of the short-distance photoelectric sensor. Among them, the optical structure is designed to collect and process the optical signal of the detected object. Based on this, the I/V conversion is needed after collecting the optical signal by PSD, and then the signal processing is carried out. The fuzzy PID control model is used to solve the problem that the control object cannot accurately establish the mathematical model. The designed photoelectric sensor is used for the handling robot, and the D-H mathematical model of the handling robot is established. The Robotics toolbox provided by MatLab is used to simulate the movement of the robot, and the sensor is used to detect the target of the robot grabbing. The experimental results show that the sensor uploads the coordinate information of the object to be grabbed to the software interface, and the handling robot can accurately grab the object with the help of the photoelectric sensor, and place it in the corresponding position according to the appearance color of different objects to be grabbed.

Hyperspectral Target Detection Methods Based on Statistical Information: The Key Problems and the Corresponding Strategies

Target detection is an important area in the applications of hyperspectral remote sensing. Due to the full use of information of the target and background, target detection algorithms based on the statistical characteristics of an image are always occupy a dominant position in the field of hyperspectral target detection. From the perspective of statistical information, we firstly presented detailed discussions on the key factors affecting the target detection results, including data origin, target size, spectral variability of target, and the number of bands. Further, we gave the corresponding strategies for several common situations in the practical target detection applications.

An Improved Lightweight Dense Pedestrian Detection Algorithm

Due to the limited memory and computing resources in the real application of target detection, the method is challenging to implement on mobile and embedded devices. In order to achieve the balance between detection accuracy and speed in pedestrian-intensive scenes, an improved lightweight dense pedestrian detection algorithm GS-YOLOv5 (GhostNet GSConv- SIoU) is proposed in this paper. In the Backbone section, GhostNet is used to replace the original CSPDarknet53 network structure, reducing the number of parameters and computation. The CBL module is replaced with GSConv in the Head section, and the CSP module is replaced with VoV-GSCSP. The SloU loss function is used to replace the original IoU loss function to improve the prediction box overlap problem in dense scenes. The model parameters are reduced by 40% and the calculation amount is reduced by 64% without losing the average accuracy, and the detection accuracy is improved by 0.5%. The experimental results show that the GS-YOLOv5 can detect pedestrians more effectively under limited hardware conditions to cope with dense pedestrian scenes, and it is suitable for the online real-time detection of pedestrians.

Modified rank sum nonparametric CFAR to combat clutter edge

The classical rank sum (RS) nonparametric constant false alarm rate (CFAR) detector plays an important role in the theoretical study and practical application of radar target detection. In order to improve the ability of the classical RS nonparametric detector to control the false alarm rate at clutter edges, a modified rank sum (MRS) nonparametric CFAR based on the mean ratio of the samples in the leading and lagging windows is proposed. The analytical expressions of the detection probability and false alarm rate of the MRS nonparametric CFAR in homogeneous background and at clutter edges are derived, and a comparison to the performance of the classical RS nonparametric detector along with some conventional parametric CFAR schemes in homogeneous background, multiple targets situation and clutter edges is made. The numerical results show that the detection performance of the MRS nonparametric CFAR in homogeneous background and in a moderate number of interfering targets situation is close to that of the classical RS nonparametric detector, and its ability to control the rise of the false alarm rate at clutter edges is evidently improved.

Review of Small Target Detection based on Deep Learning

With a large number of applications of target detection in daily life, the performance requirements of target detection are constantly improving. Many challenges about target detection have been put forward constantly, such as imbalanced samples, fewer pixels, and occlusion of the detected target, all of which bring difficulties for the model to correctly identify the target. Small target detection has always been a difficult point and research hotspot. In recent years, many algorithms for small target detection have been proposed, such as data enhancement, feature fusion, attention mechanism, and super-resolution network structure. According to the characteristics of different network structures, the training strategy can be appropriately adjusted and then applied to different environments, which can greatly improve the detection accuracy of small targets. This paper will introduce the data sets and related small target detection algorithms proposed in recent years, and classify, analyze, and compare the corresponding training strategies.