Performance Analysis Of Detection Research Articles

A Study on GAN-Based Car Body Part Defect Detection Process and Comparative Analysis of YOLO v7 and YOLO v8 Object Detection Performance

The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, compares and analyzes the object detection performance of the YOLO v7 and v8 models to present an optimal solution for detecting defects in car parts. Through experiments, the dataset was expanded by adding fake defect images generated by the GAN. The performance experiments of the YOLO v7 and v8 models based on the data obtained through this approach demonstrated that YOLO v8 effectively identifies objects even with a smaller amount of data. It was confirmed that defects could be detected. The readout of the detection system can be improved through software calibration.

Performance analysis of tabletop single-pulse terahertz detection at rates up to 1.1 MHz

Performance analysis of tabletop single-pulse terahertz detection at rates up to 1.1 MHz

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

UV photodetectors based on W-doped ZnO thin films

W-doped ZnO thin films deposited on Si substrates with (100) orientation by sol–gel spin coating method at temperature 500 °C. W/Zn atomic ratio varies from 0% to 4%. Then, the UV detection performance analysis of p–n heterojunction UV photodetectors based on W-doped ZnO/Si is analyzed. The current–voltage curves of W-doped ZnO/Si are investigated in dark and exhibit diode-like rectifying behavior. Among doped ZnO/Si, sample with atomic ratio of W/Zn = 2% is the best candidate to study photodetector characteristics in UV range. The resulting device exhibits a rectification ratio RR of 5587 at ±5 V, a higher responsivity of 3.84 A W−1 and a photosensitivity value of 34 at 365 nm under 0.5 mW cm−2. The experimental findings reveal that the UV detection performance of the heterojunction-based photodetectors strongly dependent on the properties of metal oxide layer. The main goal of this work is to investigate the effect of W doping on the performance of ZnO/Si based photodetectors. Based on our results, it is observed that 2 at% of W dopant is the optimum amount of doping for high performance photodetector of ZnO:W/Si heterojunction thanks to the suppressed recombination ratio and enhanced carrier separation properties in the depletion zone.

Detection performance analysis of advanced direction time lightning detection system in China

Detection performance analysis of advanced direction time lightning detection system in China

Detection performance analysis for SCMA based cooperative spectrum sensing under different fading channels

Detection performance analysis for SCMA based cooperative spectrum sensing under different fading channels

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.

YOLO performance analysis for real-time detection of soybean pests

In this work, we evaluated the You Only Look Once (YOLO) architecture for real-time detection of soybean pests. We collected images of the soybean plantation in different days, locations and weather conditions, between the phenological stages R1 to R6, which have a high occurrence of insect pests in soybean fields. We employed a 5-fold cross-validation paired with four metrics to evaluate the classification performance and three metrics to evaluate the detection performance. Experimental results showed that YOLOv3 architecture trained with a batch size of 32 leads to higher classification and detection rates compared to batch sizes of 4 and 16. The results indicate that the evaluated architecture can support specialists and farmers in monitoring the need for pest control action in soybean fields.

Performance analysis of object detection and tracking methodology for video synopsis

Performance analysis of object detection and tracking methodology for video synopsis

Analysis of Detection Performance of Target Detection by Passive Interferometric Microwave Sensors (PIMSs)

Analysis of Detection Performance of Target Detection by Passive Interferometric Microwave Sensors (PIMSs)

Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Acousto-Ultrasonic Inspection of Pre-baked Carbon Anodes: Repeatability Analysis and Assessment of Damage Detection Performance

Acousto-Ultrasonic Inspection of Pre-baked Carbon Anodes: Repeatability Analysis and Assessment of Damage Detection Performance

Swish and gargle saliva sampling is a patient-friendly and comparable alternative to nasopharyngeal swabs to detect SARS-CoV-2 in outpatient settings for adults and children.

Nasopharyngeal swabs (NPS) are considered the standard specimen for SARS-CoV-2 detection by PCR. We aimed to compare detection performance and patient experience of NPS with swish and gargle saliva (SGS) to determine its potential as an accurate, easy-to-collect, and comfortable alternative. We conducted a prospective study from March to May 2021 and recruited pediatric and adult outpatients seeking COVID-19 testing. Paired NPS and 5-mL SGS were collected from each participant during the same visit and tested in parallel using validated rRT-PCR assays. Variant of concern (VOC) analysis was performed for positive NPS specimens. The participant completed the surveys regarding their experience with the two collection methods. We included 238 participants in the SARS-CoV-2 detection performance analysis. Thirty-two participants tested positive for both NPS and SGS. In comparison to NPS, the sensitivity and specificity of SGS to detect SARS-CoV-2 were 94.1% (95%CI; 80.3%-99.3%) and 97.1% (95%CI; 93.7%-98.9%), respectively. Six participants tested positive only with SGS and only two with NPS. NPS is an imperfect gold standard. Thus, when compared to the COVID-19 case, that is, a patient with positive NPS and/or SGS, the sensitivity for NPS and SGS were 85.0% and 95.0%, respectively. VOC results (n = 26) revealed that 92% of the cases were alpha. From the 238 surveys, 89.9% of the participants described SGS collection as comfortable or very comfortable compared to 15.1% for NPS; 90.2% of the participants were likely or very likely to return for SGS collection compared to 59.3% for NPS. SGS is an alternative to NPS for SARS-CoV-2 detection in adult and pediatric outpatients as it is more patient-friendly while still maintaining comparable performance.IMPORTANCEWidespread and frequent testing for COVID-19 was an important strategy to identify infected patients to isolate and control the spread of the disease during the pandemic. The nasopharyngeal swab (NPS) global supply chain and access to trained healthcare professionals for standard NPS collection were often compromised. Patient discomfort and limited access challenged health systems to reach large numbers for testing in adult and pediatric populations. Our study revealed that swish and gargle saliva (SGS) was comparable to NPS in detecting SARS-CoV-2 and more patient-friendly than NPS. Patients were more likely to repeat the test with SGS. SGS was amenable to self-collection instead of relying on skilled professionals. This comprehensive evaluation highlights the challenges of comparing the accuracy of new methods to imperfect gold standards and identifies additional patient-centric factors that should be considered when defining such standards. Thus, SGS is an advantageous alternative specimen collection for outpatient en masse testing.

Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases

Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.

The performance analysis of the laser heterodyne ultra-high frequency detection based on the optical phase-locked loop

The performance of the laser heterodyne ultra-high frequency detection based on the optical phase-locked loop is analyzed through theoretical calculations and numerical simulations, considering the residual phase error and carrier stability. Appling the Wiener–Khinchin theorem, a general expression of the residual phase variance with essential affecting factors is derived. Furthermore, we define the error gain factor based on the ultra-high frequency carrier stability, which consists of the amplitude deviation and frequency deviation. Then the model of the demodulation output with the amplitude deviation and frequency deviation is presented. According to numerical simulation results, we acquired the variation curve of the residual phase variance with the loop bandwidth and the performance of the carrier signal stability. For the residual phase variance expression, the minimum variance corresponding to the optimal parameters can be obtained through the numerical simulation. The simulation experiment proves that the numerical results are consistent with the theoretical analysis. Our model for evaluating the influence of the carrier stability on the heterodyne ultra-high frequency detection can provide powerful support for improving the demodulation accuracy. The quantitative analysis of the paper will be available for theoretical basis and guidance for laser heterodyne detection based on optical phase-locked loops.

A Covert Jamming Scheme Against an Intelligent Eavesdropper in Cooperative Cognitive Radio Networks

In this work, we design a covert jamming scheme against an intelligent Eve towards physical layer security in a cooperative cognitive radio networks. To protect the primary message from being decoded by an intelligent eavesdropper (Eve), a secondary user is picked as a friendly jammer that transmits artificial noise (AN) to the Eve. However, the intelligent Eve can detect the existence of AN, and thus the AN is inclined to be covertly transmitted at a lower detection probability, thereby mistaking the AN as background noise by the Eve. In this paper, firstly, by using a binary hypothesis testing, the detection error probability (DEP) is explicitly derived for detection performance analysis of the Eve in the perfect as well as statistical channel state information (CSI) scenario, separately. With the DEP, a covert jamming scheme is formulated to transmit the AN in both cases. Then, an optimization problem is formulated, and then recast into a two-part problem to maximize the secrecy performance with the covert jamming scheme in the perfect CSI case. Finally, the effective secrecy throughput is maximized under convert constraints in the statistical CSI case. Numerical results are provided to validate the DEP at the Eve and secrecy performance analysis.

Cyber security: performance analysis and challenges for cyber attacks detection

Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.

Wavefront detection performance analysis of plenoptic sensor

Wavefront detection performance analysis of plenoptic sensor

협력 인지 무선 네트워크 양자화 비트 수 할당기법의 감지성능분석

협력 인지 무선 네트워크 양자화 비트 수 할당기법의 감지성능분석

PROMETHEE-Based Multi-AUV Threat Assessment Method Using Combinational Weights

The assessment of multiple incoming autonomous underwater vehicles (multi-AUVs) and threat prioritization are critical to underwater defense. To solve problems troubling multi-AUV threat assessment solutions, such as difficult data analysis, high subjectivity, and rigid prioritization logic, we propose the PROMETHEE algorithm based on fusion weights calculated twice by entropy and an analytic network process (ANP), respectively. First, according to AUV detection performance and underwater confrontation situation analysis, the main criteria and indicators of threat assessment are determined. The threat assessment system is provided by unified measurement of these indicators. Then, through analysis and assessment, the weighting algorithm is designed using entropy and ANP. The subjective weight calculated based on ANP and the objective weight obtained based on the entropy method are fused twice to obtain the combined weights, and the influence of subjective and objective factors on problem analysis is considered. Finally, by analyzing the simulation results of a multi-AUV, it is proven that the proposed algorithm is scientific and effective for AUV threat assessment. According to the experimental results, accurate evaluation of the target improved by at least 10%, enabling delivery of results close to the real confrontation situation with high reliability.