Detection Algorithm Research Articles

DDoS SourceTracer: An Intelligent Application for DDoS Attack Mitigation in SDN

Software-Defined Networking (SDN) enhances network management and efficiency and is particularly effective in defending against Distributed Denial of Service (DDoS) attack through its centralized structure. Our proposed DDoS SourceTracer Application utilizes SDN to efficiently identify and mitigate DDoS attack by employing tracebacking and clustering techniques. This application uses supervised and ensemble machine learning algorithms for attack detection, and feature selection methods like the Chi-square test, ANOVA (Analysis of Variance) F-test, Correlation matrix, and Extra tree classifier to optimize the feature set. Our results show that the clustering approach outperforms traditional methods like rate limiting and blocking and effectively mitigates the attack in just 3.5 s. We used the sFlow-RT tool on the Zoo topology to perform real-time analysis and validate our application’s effectiveness during attack and normal traffic. This tool analyzes how attack traffic is impacted when using clustering and tracebacking methods to mitigate DDoS attack.

A novel method based on deep learning algorithms for material deformation rate detection

A novel method based on deep learning algorithms for material deformation rate detection

A Visual Area Detection Algorithm based on Mirror Splicing for Motorcycle Indirect Vision Test

A Visual Area Detection Algorithm based on Mirror Splicing for Motorcycle Indirect Vision Test

Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm

As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects.

Retraction Note: Support attack detection algorithm for recommendation system based on deep learning

Retraction Note: Support attack detection algorithm for recommendation system based on deep learning

Structural vibration frequency monitoring based on event camera

Compared with traditional cameras, event cameras (ECs) have the significant advantages of high temporal resolution, low data redundancy, and microsecond delay, which are beneficial in structural monitoring to extract the dense response of structures in both spatial and temporal dimensions. In this paper, the vibration frequency detection method based on ECs is studied. This study investigates vibration frequency detection methods based on ECs, and proposes two algorithms for vibration frequency detection based on event streams: marker tracking and event count. Experimental verification is conducted through forced vibration experiments. The results indicate that the event count method achieves high-precision measurement of vibration frequencies in the range of 10–190 Hz for different vibration scales, with a maximum relative error of 1% and an average relative error of 0.673%. The marker tracking method demonstrates a maximum relative error of 1.43% and an average relative error of 0.575% in frequency measurement for large-amplitude vibrations. However, as the amplitude decreases, the frequency measurement error increases. When the amplitude is less than 3 pixels, the frequency measurement error exceeds 30%, rendering the measurement results unreliable. This research provides technical support for high-precision structural vibration frequency monitoring and further expands the application of ECs in structural monitoring.

LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers.

Recent Advances for Aerial Object Detection: A Survey

Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.

A Semantic Spatial Structure-Based Loop Detection Algorithm for Visual Environmental Sensing

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods.

Vehicle Detection Algorithms for Autonomous Driving: A Review

Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends.

AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION OF IMAGES

Edge detection is a fundamental process in image processing with significant implications across multiple domains. Within the realm of object detection, the accurate delineation of image perimeters forms the bedrock for subsequent analysis. In this study, we present an optimized Laplacian Edge Detection algorithm tailored for efficient implementation in hardware.Leveraging approximation methodologies and strategic pipelining, we streamline complex operations, thereby reducing latency and resource consumption. Our implementation, realized through the Verilog Hardware Description Language (VHDL), stands as a testament to the efficacy of our approach. Through rigorous optimization and meticulous comparison with conventional Sobel edge detection, we demonstrate superior performance metrics, including enhanced Area utilization, reduced Time Delay, and improved Power Efficiency. This work underscores the importance of methodological refinement and technological innovation in advancing edge detection techniques for practical applications. Keywords: Laplacian, Sobel, Kernel, Pixel range, Area, Power

Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

A practical gestational age-based algorithm for timely detection of hypothyroidism in premature infants.

To assess utility and accuracy of a gestational age-based screening targeting premature infants to detect congenital hypothyroidism. A prospective cohort study was conducted in infants <35 weeks' gestational age with clinical outcomes at 2-3 years of age. Patients received newborn screenings at 24 hours and 10-14 days of life. Free T4 (FT4) and thyroid-stimulating hormone (TSH) levels were measured at one month of life and repeated based on algorithm by corrected gestational age. Among infants <35 weeks gestation (n = 938), the incidence of hypothyroidism requiring treatment was 1:58. TSH levels at one month of age was predictive of treatment (AUC 0.96, 95% CI 0.88-1). The optimal TSH threshold of 8 mIU/L (8 µU/ml) increased the specificity to 0.97 and sensitivity to 0.88. Following initiation of treatment forhypothyroidism during NICU hospitalization,43.8% (n = 7) were diagnosed with permanent congenital hypothyroidism. Our study supports a gestational age-based screening algorithm for early detection of hypothyroidism in premature infants.

Improved 3D human face reconstruction from 2D images using blended hard edges

AbstractThis study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination.

Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)

Cache Side Channel Attacks (CSCA) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks namely software mishandling, performance overhead, low throughput due to false alarms, etc. Hence, “mitigation only when detected” should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module. We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers, the number of victims having RSA,AES and ECC based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than \(98\% \) detection accuracy within \(2\% \) of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of \(0.9\% \) to \(2\% \) and \(1\% \) to \(2.1\% \) for the targeted RISC-V processor core without cache for 1 to 5 counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Investigating the Influence of Varied Particle Sizes on the Load-Bearing Properties of Arrester Bed Aggregates

This study employs the discrete element method to investigate the influence of particle size on the load-bearing characteristics of aggregates, with a specific emphasis on the aggregates used in escape ramp arrester beds. This study utilises the log edge detection algorithm to introduce an innovative approach for modelling irregularly shaped pebbles, integrating their physical properties into a comprehensive discrete element model to enhance the accuracy and applicability of simulations involving such pebbles. Meticulous validation and parameter calibration (friction coefficient: 0.37, maximum RMSE: 3.43) confirm the accuracy of the simulations and facilitate an in-depth examination of the mechanical interactions between aggregate particles at macroscopic and microscopic scales. The findings reveal a significant relationship between the particle size and load-bearing capacity of aggregates. Smaller pebbles, which are more flexible under pressure, can be packed more densely, thereby improving the distribution of vertical forces and increasing the concentration of local stress. This enhancement substantially increases the overall load-bearing capacity of aggregates. These discoveries hold significant implications for engineering practices, particularly in the optimisation of safety for truck escape ramps and in identifying the ideal sizes of pebbles with irregular shapes.

An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images

A YOLOv5-based YOLOv5-KE unmanned aerial vehicle (UAV) image detection algorithm is proposed to address the low detection accuracy caused by the small size, high density, and overlapping leaves of Ambrosia trifida targets in UAV images. The YOLOv5-KE algorithm builds upon the YOLOv5 algorithm by adding a micro-scale detection layer, adjusting the hierarchical detection settings based on k-Means for Anchor Box, improving the loss function of CIoU, reselecting and improving the detection box fusion algorithm. Comparative validation experiments of the YOLOv5-KE algorithm for Ambrosia trifida recognition were conducted using a self-built dataset. The experimental results show that the best detection accuracy of Ambrosia trifida in UAV images is 93.9%, which is 15.2% higher than the original YOLOv5. Furthermore, this algorithm also outperforms other existing object detection algorithms such as YOLOv7, DC-YOLOv8, YOLO-NAS, RT-DETR, Faster RCNN, SSD, and Retina Net. Therefore, YOLOv5-KE is a practical algorithm for detecting Ambrosia trifida under complex field conditions. This algorithm shows good potential in detecting weeds of small, high-density, and overlapping leafy targets in UAV images, it could provide technical reference for the detection of similar plants.

Object Detection in Hazy Environments, Based on an All-in-One Dehazing Network and the YOLOv5 Algorithm

This study introduces an advanced algorithm for intelligent vehicle target detection in hazy conditions, aiming to bolster the environmental perception capabilities of autonomous vehicles. The proposed approach integrates a hybrid convolutional module (HDC) into an all-in-one dehazing network, AOD-Net, to expand the perceptual domain for image feature extraction and refine the clarity of dehazed images. To accelerate model convergence and enhance generalization, the loss function has been optimized. For practical deployment in intelligent vehicle systems, the ShuffleNetv2 lightweight network module is incorporated into the YOLOv5s network backbone, and the feature pyramid network (FPN) within the neck network has been refined. Additionally, the network employs a global shuffle convolution (GSconv) to balance accuracy with parameter count. To further focus on the target, a convolutional block attention module (CBAM) is introduced, which helps in reducing the network’s parameter count without compromising accuracy. A comparative experiment was conducted, and the results indicated that our algorithm achieved an impressive mean average precision (mAP) of 76.8% at an intersection-over-union (IoU) threshold of 0.5 in hazy conditions, outperforming YOLOv5 by 7.4 percentage points.