Target Detection Applications Research Articles

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.

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

BackgroundArtificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interventional radiology has the potential to increase the efficacy and cost-efficiency of interventional procedures while improving postsurgical outcomes and reducing the burden for medical personnel.MethodsTo overcome the lack of available clinical data needed to train state-of-the-art AI models, we propose a novel approach for generating synthetic ultrasound data from real, clinical preoperative three-dimensional (3D) data of different imaging modalities. With the synthetic data, we trained a deep learning-based detection algorithm for the localization of needle tip and target anatomy in US images. We validated our models on real, in vitro US data.ResultsThe resulting models generalize well to unseen synthetic data and experimental in vitro data making the proposed approach a promising method to create AI-based models for applications of needle and target detection in minimally invasive US-guided procedures. Moreover, we show that by one-time calibration of the US and robot coordinate frames, our tracking algorithm can be used to accurately fine-position the robot in reach of the target based on 2D US images alone.ConclusionsThe proposed data generation approach is sufficient to bridge the simulation-to-real gap and has the potential to overcome data paucity challenges in interventional radiology. The proposed AI-based detection algorithm shows very promising results in terms of accuracy and frame rate.Relevance statementThis approach can facilitate the development of next-generation AI algorithms for patient anatomy detection and needle tracking in US and their application to robotics.Key points• AI-based methods show promise for needle and target detection in US-guided interventions.• Publicly available, annotated datasets for training AI models are limited.• Synthetic, clinical-like US data can be generated from magnetic resonance or computed tomography data.• Models trained with synthetic US data generalize well to real in vitro US data.• Target detection with an AI model can be used for fine positioning of the robot.Graphical

Polarization-Multiplexed High-Throughput AOTF-Based Spectral Imaging System.

Spectral imaging detection using acousto-optical tunable filters (AOTFs) faces a significant challenge of low throughput due to the traditional design that only receives a single polarization light. To overcome this issue, we propose a novel polarization multiplexing design and eliminate the need for crossed polarizers in the system. Our design allows for simultaneous collection of ±1 order light from the AOTF device, resulting in a more than two-fold increase in system throughput. Our analysis and experimental results validate the effectiveness of our design in improving system throughput and enhancing the imaging signal-to-noise ratio (SNR) by approximately 8 dB. In addition, AOTF devices used in polarization multiplexing applications require optimized crystal geometry parameter design that does not follow the parallel tangent principle. This paper proposes an optimization strategy for arbitrary AOTF devices which can achieve similar spectral effects. The implications of this work are significant for target detection applications.

A split-type photoelectrochemical immunosensing platform based on atom-efficient cation exchange for physiological monitoring

Ultrasensitive and accurate physiological monitoring is of great significance for disease diagnosis and treatment. In this project, an efficient photoelectrochemical (PEC) split-type sensor on the basis of controlled release strategy was established with great success. Heterojunction formation between g-C3N4 and Zn-doped CdS improved the visible light absorption efficiency, reduced carrier complexation, improved the PEC signal, and increased the stability of the PEC platform. Compared to the traditional model of immunosensors, the process of antigen-antibody specific binding was done in a 96 microplate, and the sensor separated the immune reaction from the photoelectrochemical conversion process, eliminating mutual interference. Cu2O nanocubes were used to label the second antibody (Ab2), and acid etching using HNO3 released a large amount of divalent copper ions, which exchanged cations with Cd2+ in the substrate material, causing a sharp drop in photocurrent and improving the sensitivity of the sensor. Under the optimized experimental conditions, the PEC sensor based on the controlled release strategy for CYFRA21-1 target detection had a wide concentration linear range of 5 × 10−5 to 100 ng/mL with a low detection limit of 0.0167 pg/mL (S/N = 3). This intelligent response variation pattern could also offer the possibility of additional clinical applications for other target detection.

Improving the ranging performance of chaos LiDAR.

Chaos lidar has gained significant attention due to its high spatial resolution, natural anti-interference capability, and confidentiality. However, constrained by the power of the chaos laser, the sensitivity of the linear detector, and the hardware bandwidth, chaos lidar is greatly restricted in the application of long-distance target detection and imaging. To overcome these constraints, we propose a novel, to the best of our knowledge, chaos lidar based on Geiger mode avalanched photodetectors (GM-APDs) in a previous study called chaos single-photon (CSP) lidar. In this paper, we compare the CSP lidar with the linear mode chaos lidars by combining with lidar equation. Regarding the ranging principle, the CSP lidar is fully digital and breaks through the constraints of a detector's bandwidth and ADC's sampling rate. The simulation results indicate that the detection range of the CSP lidar is approximately 35 times and 8 times greater than that of a continuous-wave chaos lidar and pulsed chaos lidar, respectively. Although the detection accuracy of the CSP lidar is only at the centimeter level and is lower than the linear mode chaos lidars, its consumption of storage resources and power is greatly reduced due to 1-bit quantization in the GM-APD. Additionally, we investigate the impact of GM-APD parameters on the signal-to-noise ratio (SNR) of the CSP lidar system and demonstrate that the dead time difference between GM-APDs has a negligible effect. In conclusion, we present and demonstrate a new chaos lidar system with a large detection range, high SNR, low storage resources and power consumption, and on-chip capability.

Analysis of Influence of the Seamount in the South China Sea on Sound Propagation

The influence of seamounts on sound propagation is very important, and it is also a research hotspot for hydroacoustic. This paper uses the BELLHOP ray model to analyze the influence of seamounts in the South China Sea on sound propagation, it is found that the blocking of seamounts has a greater impact on the spatial distribution of the transmission loss. When forward propagation, the distribution of the transmission loss is mainly influenced by seamounts, and its temporal variation is not obvious. When the sound source is relatively far away from the first seamount, the first seamount does not have a great influence on the distribution of the transmission loss. When backward propagation, the transmission loss increases rapidly after passing through the first seamount. Most of the sound energy is blocked due to the higher height of the first seamount. In addition, the impact of the undulating bottom on the front of seamounts on sound propagation should also not be ignored during backward propagation. The seamount determines the most important characteristics of the transmission loss distribution, while the undulating bottom has a certain degree of influence on the distribution characteristics of some specific regions. The above results have important applications for target detection and hydroacoustic communication in the area of seamounts.

Hyperspectral Image Fusion Algorithm Based on Improved Deep Residual Network

High-resolution hyperspectral images are rich in both spatial and spectral information and have numerous applications for target detection, recognition, and tracking. The method proposed in this paper consists of three specific modules: multi-scale input, improved residuals, down-sampling, and two-channel pooling. By using wider and deeper blocks of residuals, the classification accuracy of the model is significantly improved. To increase the depth of the network, 1×1 convolutions were added to the 3×3 convolutions compared to the basic residual block. This makes it easier to control the number of channels so that the information sensed and the features extracted are different during each convolution. In addition to the Elu activation function, the Dropout layer and the batch normalization layer for every 3×3 convolutions in the network, the residual network is enhanced by using two stacked residual blocks after the two-channel pooling layer. Extensive experiments on many well-known benchmark datasets have shown that the proposed method achieves competitive performance.

YOLO Series Target Detection Technology and Application

Recently, YOLO is the most popular algorithm in machine learning. The algorithm has developed rapidly, and there are several versions at present. Each version of the framework is different, and they also have their own application areas. And maybe in one area, not only one version can be used. This paper summarizes the process of target detection, the structures of YOLO network. In addition, this work also analyzed the development, advantages and disadvantages of YOLO target detection. Finally, the application of YOLO in automatic driving and UAV detection are discussed. YOLO may develop faster in the future. YOLO model is a variable model, which has unique functions when detecting different things under different circumstances. At the end of the paper, the thesis is summarized, and the related research has certain reference value.

Scattering analysis of a Laguerre Gaussian beam for target detection.

The propagation and scattering characteristics of a Laguerre Gaussian (LG) beam and Gaussian beam are theoretically and experimentally comparatively studied. The phase of the LG beam is almost free of scattering when the scattering is weak, resulting in a much weaker loss of transmission compared with the Gaussian beam. However, when scattering is strong, the phase of the LG beam is completely disturbed by it, and the transmission loss of the LG beam is stronger than that of the Gaussian beam. Moreover, the phase of the LG beam becomes stabler as the topological charge increases, and the beam radius increases as well. Therefore, the LG beam is suitable for short-range target detection in a weak scattering medium, not for long-range in a strong scattering medium. This work will contribute to the development of target detection, optical communication, and other applications of orbital angular momentum beams.