Performance Analysis Of Detection Research Articles

Comparing Human Performance on Target Localization in Near Infrared and Long Wave Infrared for Cluttered Environments.

In the context of rapid advancements in AI, the accuracies and speeds among various AI models and methods are often compared. However, a basic question is rarely asked: is AI better than humans, and if so, under what conditions? This paper investigates human ability to detect distant landmark targets under cluttered surroundings such as buildings, trees, and clouds in NIR and LWIR images, aiming to facilitate AI object detection performance analysis. Our investigation employs perception tests and a human performance model to analyze object detection capabilities. The results reveal distinctive differences in NIR and LWIR detectability, showing that although LWIR performs less effectively at range, it offers superior robustness across various environmental conditions. Our findings suggest that AI could be particularly advantageous for object detection in LWIR as it outperform humans in terms of detection accuracy at a long range.

Performance Analysis of Anomaly-Based Network Intrusion Detection Using Feature Selection and Machine Learning Techniques

Performance Analysis of Anomaly-Based Network Intrusion Detection Using Feature Selection and Machine Learning Techniques

Comparative analysis of UAV detection and tracking performance: Evaluating YOLOv5, YOLOv8, and YOLOv8 DeepSORT for enhancing anti-UAV systems

This article presents a comprehensive comparative analysis of the performance of three prominent object detection and tracking models, namely YOLOv5, YOLOv8, and YOLOv8 DeepSORT, in the domain of UAV detection and tracking. The study aims to assess the effectiveness of these models in enhancing anti-UAV systems. A series of experiments were conducted using diverse datasets and evaluation metrics to evaluate the detection and tracking capabilities of each model. The results provide valuable insights into the strengths and limitations of YOLOv5, YOLOv8, and YOLOv8 DeepSORT, shedding light on their potential applications in anti-UAV systems. The findings of this study contribute to the advancement of UAV detection and tracking technologies and serve as a guide for researchers and practitioners in the field of anti-UAV systems.

Performance Analysis of Light Detection and Ranging (LIDAR) in Augmented Reality (AR)

Augmented reality (AR) has existed for decades, dating back from roughly 50 years ago when the renowned ‘father of computer graphics’ Ivan Sutherland first invented an AR head-mounted display system. Ever since, various organizations strive to improve the technology but while the AR technology definitely evolved significantly since its advent, the technology progress is relatively stagnant these past few years whereby we see little to no usage at least from common end user perspective. This may be due to various limitations of technologies currently utilized in AR. However, things may change since in 2020, the tech giant, Apple Inc. has made an unprecedented breakthrough by including the light detection and ranging (LiDAR) scanner on consumers’ smartphones which could potentially revolutionize the AR industry. This research primarily revolves around analyzing the performance of utilization of LiDAR technology in AR mainly in mobile devices available for the mass. The methodology and concept for this research is fairly straight-forward, the AR experience boosted by the newly introduced LiDAR technology will be compared with the widely used technology which primarily consist of the implementation of Simultaneous Localization and Mapping (SLAM) and RGB camera with the exclusion of LiDAR and assessing the potential of LiDAR to further improvise AR experience and implementation in various fields. Testing results has shown significant improvement from various important aspects in AR whereby occlusion is possible in object rendering, the estimations based on live visual stimuli accuracy at roughly 99% in comparison to the true values as well as the ability to accurately perceive the geometry of objects in the real world environment through advanced depth and space perception.

Thermal Battery Multi-Defects Detection and Discharge Performance Analysis Based on Computed Tomography Imaging

To address the typical structural defects that are prone to occur during the preparation and storage processes of thermal battery, experiments of battery image acquisition were designed based on X-ray computed tomography system. An improved Yolov5s network was employed to achieve high-precision automatic detection of typical defects. Through the discharge experiment of thermal battery, discharge performance curves of normal batteries and three defective batteries were constructed. The impact and mechanisms of different defects on the discharge performance were analyzed based on the voltage curve. By designing an automatic stitching scheme, the phenomenon of interlayer information overlap caused by the increase of cone angle in digital radiography images was suppressed. To address the issues of low image contrast and limited defect data in thermal battery imaging, the defect dataset was expanded using the designed image preprocessing steps and improving the contrast of the images. For subtle defects that are difficult to identify, the introduced multi-head self-attention mechanism in Transformer and the use of Focal Loss instead of cross-entropy loss function were employed to improve the recognition accuracy of subtle defects while ensuring the detection speed. The comparative experiment shows that the improved network model has higher recognition accuracy compared to Faster R-CNN, SSD, Cascade R-CNN, EfficientDet and the original Yolov5s network. The recognition accuracy of typical defects in thermal batteries can reach 98.7%.

Performance analysis of network intrusion detection using T-Pot honeypots

Performance analysis of network intrusion detection using T-Pot honeypots

Incoherent Detection Performance Analysis of the Distributed Multiple-Input Multiple-Output Radar for Rice Fluctuating Targets

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations.

Performance analysis of lung cancer detection and classification using efficientNet: a deep learning model

Performance analysis of lung cancer detection and classification using efficientNet: a deep learning model

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.

Detection Performance Analysis of Marine Wind by Lidar and Radar under All-Weather Conditions

Accurate marine wind detection under all-weather conditions is crucial for maritime activities. The joint detection of lidar and radar is supposed to be a potential way to carry out the all-weather sensing of wind. However, their performance analysis has not been well studied, particularly in the far sea area, where the wind-tracing particles are quite different from those inland. Based on the particle distributions above the sea surface under different weather conditions, this study investigated the scattering and attenuation effects of lidar and radar waves in open sea areas with the Mie theory and T-matrix method. Then, the maximum detection range and velocity accuracies of lidar/radar were comprehensively analyzed based on detection principles to optimize the combination of lidar and radar. According to the simulation results, it was difficult to maintain the detection capability of a single lidar/radar under all-weather conditions, and 1.55 μm lidar and W-band radar presented a promising joint detection scheme, as they exhibited optimal weather adaptability in clear sky and precipitation conditions, respectively.

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 Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

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)