Efficient Detection Algorithm Research Articles

ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model

As the application of networks permeates various aspects of daily life, maintaining network security has become a crucial challenge. A network intrusion detection system (NIDS) functions as a critical technique for securing cyberspace and has gained considerable attention. Although researchers have made significant progress in developing NIDSs, challenges still exist in high-speed networks with overwhelming network traffic. Existing methods largely focus on improving model detection accuracy and often overlook speed and computational efficiency. This oversight renders most current methods impractical for real-world high-speed network scenarios. To address this issue, we propose an innovative and efficient network intrusion detection algorithm, namely, the Bayesian gamma mixture model (GaMM) classifier. With the recently proposed extended stochastic variational inference (ESVI) framework, we introduce lower-bound approximations to the evidence lower bound (ELBO), namely, the original variational object function. An analytically tractable Bayesian estimation algorithm for a GaMM is derived through stochastic optimization of the obtained lower bound and we validate its performance and computational efficiency on three publicly available datasets (CICMalmem2022, OPCUA, and CICIDS2018). The experimental results indicate that the proposed classifier not only achieves a detection performance comparable to that of other benchmark models but also significantly reduces both the training and detection times.

A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces

Automating face mask detection in public areas is paramount to maintaining public health, especially in the context of the COVID-19 pandemic. Utilization of technologies such as deep learning and computer vision systems enables effective monitoring of mask compliance, thereby minimizing the risk of virus spread. Real-time detection helps in prompt intervention for and enforcement of the use of masks, thereby preventing potential outbreaks and ensuring compliance with public health guidelines. This method helps save human resources and makes the reinforcement of wearing masks in public areas consistent and objective. Automatic detection of face masks serves as a key tool for preventing the spread of contagious diseases, protecting public health, and creating a safer environment for every person. This study addresses the challenges of real-time face mask detection via drone surveillance in public spaces, with reference to three categories: wearing of mask, incorrect wearing of mask, and no mask. Addressing these challenges entails an efficient and robust object detection and recognition algorithm. This algorithm can deal with a crowd of multiple faces via a mobile camera carried by a mini drone, and performs real-time video processing. Accordingly, this study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The C-Mask model aims to operate within a mini drone surveillance system and provide efficient and robust face mask detection. The C-Mask model performs preprocessing, feature extraction, feature generation, feature enhancement, feature selection, and multivariate classification tasks for each face mask detection cycle. The preprocessing task prepares the training and testing data in the form of images for further processing. The feature extraction task is performed using a Convolutional Neural Network (CNN). Moreover, Cross-Stage Partial (CSP) DarkNet53 is used to improve the feature extraction and to facilitate the model’s object detection ability. A data augmentation algorithm is used for feature generation to enhance the model’s training robustness. The feature enhancement task is performed by applying the Path Aggregation Network (PANet) and Spatial Pyramid Pooling Network (SPPNet) algorithms, which are deployed to enhance the extracted and generated features. The classification task is performed through multi-label classification, wherein each object in an image can belong to multiple classes simultaneously, and the network generates a grid of bounding boxes and corresponding confidence scores for each class. The YOLO-based C-Mask model testing is performed by experimenting with various face mask detection scenarios and with varying mask colors and types, to ensure the efficiency and robustness of the proposed model. The C-Mask model test results show that this model can correctly and effectively detect face masks in real-time video streams under various conditions with an overall accuracy of 92.20%, precision of 92.04, recall of 90.83%, and F1-score of 89.95%, for all the three classes. These high scores have been obtained despite mini drone mobility and camera orientation adjustment substantially affecting face mask detection performance.

PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning

The detection of pomegranate fruitlets is of great significance for fruit thinning and yield estimation. However, the researches on the detection of pomegranate fruitlets are rarely considered in recent years. In addition, the immature fruit detection algorithms often have a large amount of calculation, which require high computing memory and computing resources of the equipment. Based on the mentioned issue, we propose a lightweight detection method based on the improved You Only Look Once Version 8(YOLOv8) to realize the automatic detection of pomegranate fruitlets (PG-YOLO). Firstly, the lightweight network Shufflenetv2 is used to reconstruct the backbone of YOLOv8s. Secondly, the Depthwise separable convolution is used to replace the standard convolution of Neck layer. Then, the multi-head self-attention (MHSA) is used to enhance the positioning ability and feature extraction ability of the model before the backbone network outputs features. Finally, the self-built pomegranate fruitlet dataset is used to train and test the PG-YOLO model. It is shown that, the Mean Average Precision(mAP) of pomegranate fruitlets detected by PG-YOLO model is 93.4%. The optimal weight size of PG-YOLO is only 2.2 Mb, which is 89.9% less than the original YOLOv8s. In addition, the detection speed of the PG-YOLO model is 74.1% higher than the original YOLOv8s. The results demonstrate that the study significantly reduces the complexity of the model and provides a research basis for further application in mobile devices. The self-built PG-YOLO-Dataset used in this paper has been published on github: https://github.com/LforikC/PG-YOLO-Dataset.

GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

GRFS-YOLOv8: an efficient traffic sign detection algorithm based on multiscale features and enhanced path aggregation

Employing feature mixture for active learning of object detection

Active learning aims to select the most informative samples for annotation from a large amount of unlabeled data, in order to reduce time-consuming and labor-intensive manual labeling efforts. Although active learning for object detection has made substantial progress in recent years, developing an accurate and efficient active learning algorithm for object detection remains a challenge. In this paper, we propose a novel unsupervised active learning method for deep object detection. This is based on our hypotheses that an object is more likely to be wrongly predicted by the model, if the prediction changes when its feature representations are slightly mixed by another feature representations at a very small ratio. Such unlabeled samples can be regarded as informative samples that can be selected by active learning. Our method employs base representations of all categories generated from the object detection network to examine the robustness of every detected object. We design a scoring function to calculate the informative score of each unlabeled image. We conduct extensive experiments on two public datasets, i.e., PASCAL VOC and MS-COCO. Experiment results show that our approach consistently outperforms state-of-the-art single-model based methods with significant margins. Our approach also performs on par with multi-model based methods, at much lesser computational cost.

An efficient intrusion detection technique for traffic pattern learning

Efficient intrusion detection algorithms are required for network traffic learning patterns in order to protect advanced network communication channels. These systems can be used to detect normal and unusual patterns, signatures, and rule violations. In recent years, conventional and deep machine learning algorithms have been utilized in the field of network intrusion detection for network traffic learning systems. The use of machine learning opens up new attack surfaces that are very intriguing to investigate. Attackers can introduce noisy data into training data to influence testing patterns in computer networks. The goal of this work is to create an efficient intrusion detection solution for network traffic learning patterns using a supervised and unsupervised technique. We developed an effective intrusion detection system (IDs) using an appropriate NSLKDD dataset for network traffic patterns. The model was trained and evaluated using the Genetic Optimization Algorithm (GOA) and the Niave Bayesian technique to recognize usual and unexpected network traffic patterns. We created a strategy that begins with a random population and subsequent iterates through the fitness function, returning the best parents with high detection accuracy. The best parents were determined using the n-parameters iterated by the crossover and mutation procedures. A cross over function was created to combine genes from two fitness parents by randomly selecting portions from each parent. The individual components of the crossover offsprings are randomly flipped to achieve the mutation. The fitness of the previous generation was obtained to generate a new generation, and this process was repeated n times. This was created to detect network intrusions using Nave Bayes' binary categorization problem and evolutionary algorithms. We accomplished this task by aggregating noise into training set before broadcasting the average number, and it is critical not to have that public average too frequently. The experimental results reveal that our proposed GA fared better than the NB technique, with a detection accuracy of 95.0% versus a recommendable detection accuracy of 53.0%.

A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small‐scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU‐YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small‐scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi‐level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU‐YOLOv5 achieves a 7.4% improvement in mAP@0.5 and a 4.9% improvement in mAP@0.5:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

DRIVER DROWSINESS DETECTION AND CREATING SAFETY MEASURES USING OPENCV,PYTHON

Accidents due to driver drowsy driving is significantly increasing in high ratio now days. So we proposed an AI based solution to reduce the driver drossy driving accident by alerting the driver when they fall asleep. This is an brief abstract that explains system working and principles. This paper present a novel, Real-Time Driver Drowsiness Detection System(DDDS) that utilizes a multi-model approach for fatigue assessment. The system incorporates computer vision and machine learning techniques to analyze facial features, eye movements, and physical signal(if applicable) captured through a webcam processing techniques enhance data processing techniques enhanced data quality for better analysis.Facial landmarks are identified to locate the eyes, and features like Eye Aspect Ratio(EAR) are used to detect signs of fatigue. The system employs a multimodal approach, utilizing computer vision techniques to analyze facial features and eye movements captured through a webcam. Specifically, the DDDS leverages the Haar cascade algorithm for efficient facial landmark detection and the Histogram of Oriented Gradients (HOG) algorithm for feature extraction from facial regions of interest. This combination allows for accurate localization and analysis of key features like eyes. Additionally, the system explores the potential of physiological data (e.g., heart rate variability) for a more comprehensive analysis (if applicable). Machine learning techniques (if applicable) can be employed on the combined features, including those extracted using Haar and HOG, for enhanced fatigue classification. Upon detecting drowsiness, the DDDS triggers customizable alerts (visual, audio, or haptic) to warn the driver. The system additionally explores the potential of physiological data (e.g., heart rate variability) for fatigue detection, offering a more comprehensive approach (if applicable).A machine learning model can be employed on the combined features for robust fatigue classification. Upon detection of drowsiness,the DDDS triggers customizable alerts ( visual, audio, or haptic) to warn the driver. This system addresses limitations of existing solution by: 1.Real-Time processing:Ensuring immediate detection and response to fatigue signs. 2.Multi feature analysis: Combining facial features, eye movements,and physiological data for robust detection. 3.Customizable alerts: Providing drivers control over alert type for a personalized experience. The evaluation methodology employs a dataset of [data type e.g.., image, videos] to access the system’s performance.Metrics like accuracy,precision,recall,and F1-score will be used to evaluate the effectiveness of the DDDS in detecting drowsy driving. This research aims to contribute to safer roads by developing a comprehensive and adaptable Driver Drowsiness Detection System. II.KEYWORDS: Drowsy Driving, Driver Monitoring,Computer Vision, Machine Learning,Eye Tracking, Fatigue Detection,Physiological Signs(if applicable)

Hailstorm events in the Central Andes of Peru: insights from historical data and radar microphysics

Abstract. Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017–2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (−0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru.

Comparative Study of Machine Learning Algorithms for Traffic Sign Recognition

The increasing integration of intelligent transportation systems has brought forth a critical need for accurate and efficient traffic sign detection algorithms. In this research, we conduct a comprehensive comparative analysis of four prominent convolutional neural network (CNN) architectures—LeNet, ResNet, VGG, and a standard CNN model—for their performance in traffic sign detection. The evaluation is conducted on a diverse dataset, utilizing the German Traffic Signs Recognition Benchmark (GTSRB). Our study encompasses an in-depth examination of accuracy metrics and performance under varying conditions, such as challenging lighting, occlusion, and environmental complexities. The results demonstrate nuanced differences in accuracy and computational efficiency among the algorithms. LeNet exhibits notable improvements in real-time processing, while ResNet showcases enhanced accuracy, and VGG demonstrates robustness in challenging conditions. The findings provide valuable insights into the strengths and weaknesses of each algorithm, aiding researchers and practitioners in selecting the most suitable model for specific traffic sign detection applications. This research contributes to advancing the field of computer vision in intelligent transportation systems, with implications for enhancing road safety and efficiency. Key Words: traffic sign recognition, deep learning, Res-Net, VGG19, CNN, LeNet.

An efficient algorithm for multi-scale maritime object detection and recognition

Due to the complexity of the maritime environment and the diversity of the volume and shape of monitored objects in the maritime, existing object detection algorithms based on Convolutional Neural Networks (CNN) are challenging to balance the requirements of high accuracy and high real-time simultaneously in the field of maritime object detection. In response to the characteristics of complex backgrounds, significant differences in object size between categories, and the characteristic of having a large number of small objects in maritime surveillance videos and images, the Maritime dataset with rich scenes and object categories was self-made, and the OS-YOLOv7 algorithm was proposed based on the YOLOv7 algorithm. Firstly, a feature enhancement module named the TC-ELAN module based on the self-attention mechanism was designed, which enables the feature map used for detection to obtain enhanced semantic information fused from multiple scale features. Secondly, in order to enhance the attention to the area of dense small objects and further improve the positioning accuracy of occluded small objects, this study redesigned the SPPCSPC structure. Then, the network structure was improved to alleviate the problem of decreased object detection accuracy caused by the loss of semantic feature information. Finally, experimental results on self-made datasets and mainstream maritime object detection datasets show that OS-YOLOv7 has a better object detection effect compared to other state-of-the-art (SOTA) object detection algorithms at the cost of reasonable inference time and parameter quantity and can achieve good object detection accuracy on mainstream datasets with high real-time performance.

A Systematic Review of the Application of Machine Learning in CpG Island (CGI) Detection and Methylation Prediction

Background: CpG island (CGI) detection and methylation prediction play important roles in studying the complex mechanisms of CGIs involved in genome regulation. In recent years, machine learning (ML) has been gradually applied to CGI detection and CGI methylation prediction algorithms in order to improve the accuracy of traditional methods. However, there are a few systematic reviews on the application of ML in CGI detection and CGI methylation prediction. Therefore, this systematic review aims to provide an overview of the application of ML in CGI detection and methylation prediction. Methods: The review was carried out using the PRISMA guideline. The search strategy was applied to articles published on PubMed from 2000 to July 10, 2022. Two independent researchers screened the articles based on the retrieval strategies and identified a total of 54 articles. After that, we developed quality assessment questions to assess study quality and obtained 46 articles that met the eligibility criteria. Based on these articles, we first summarized the applications of ML methods in CGI detection and methylation prediction, and then identified the strengths and limitations of these studies. Result: Finally, we have discussed the challenges and future research directions. Conclusion: This systematic review will contribute to the selection of algorithms and the future development of more efficient algorithms for CGI detection and methylation prediction

Detection and location of high impedance faults in delta 13.8kV distribution networks

This article presents the main results obtained in the project “Automated System of Classification, Detection, and Location of High Impedance Faults in Distribution Networks”, carried out for Brazilian utility COPEL, between 2019 and 2021, as part of ANEEL's R&D program. The project developed an automated system with efficient and robust algorithms for the real-time detection, classification, and location of high impedance faults in the distribution networks. The system consisted of computational tools to automatic high impedance faults detection and location in 13.8 kV delta-connected grid. Field tests carried out in a real feeder turned possible the development of a simple and reliable algorithm, related to the amplitude and phase shift of the zero-sequence current monitored in the substation. The fault location was determined by consulting undervoltage at automatic reclosers, and smart energy meters installed along the feeder. A computational tool was developed to provide relevant information to the utility Operation Center to initiate an emergency maintenance.

Efficient genetic algorithm for spam mail detection and classification

E-mail is the quickest tool to convey information from one to others. Individual users and organizations have become more reliant on e-mails as technology advances. At the moment, all email inboxes are swamped with spam and these cause significant and diverse difficulties, such as the loss of crucial information and the theft of the recipient’s identity. Organizations may suffer significant losses. As a result, users cannot avoid spam emails, which come in various formats such as advertisements and messages. Spam filtering removes spam messages and prevents them from being accessed. This paper focuses on categorization of e-mails by using genetic algorithm. The proposed approach uses entropy to evaluate information gain, which is subsequently employed by the genetic algorithm classifier to choose important features from the spam database. The model performance is then assessed by using the testing dataset with all 57 features, a crossover probability of 0.3, and a mutation probability of 1.0 and obtained good accuracy.

Phase sensitive detection for embedded sensors

The measurement of some physical properties requires detection of both amplitude and phase of an applied test signal. In portable sensors, this needs to be performed locally with integrated analog circuits or digital processing. Wireless sensor network allows the measurements over wide areas but raises a challenge of using efficient algorithm for phase sensitive detection (PSD) in nodes with constrained processing capacity. It is not straightforward to compare different PSD methods because their performance is closely related to the fine tuning of their parameters such as filter order and cut-off frequency. We propose a methodology to compare several PSD methods subjected to the same response time to assess its ability to provide accurate estimation in the presence of noise. We also determine the computational complexity of the investigated methods in terms of the number of operations required. We investigate both continuous and windowed operation. The methods were tested through simulations and verified using a dedicated embedded system. Our findings demonstrate that Goertzel algorithm rendered the best results, with smaller estimation error and less computation complexity.

An efficient unused integrated circuits detection algorithm for parallel scan architecture

In recent days, many integrated circuits (ICs) are operated parallelly to increase switching operations in on-chip static random access memory (SRAM) array, due to more complex tasks and parallel operations being executed in many digital systems. Hence, it is important to efficiently identify the long-duration unused ICs in the on-chip SRAM memory array layout and to effectively distribute the task to unused ICs in SRAM memory array. In the present globalization, semiconductor supply chain detection of unused SRAM in large memory arrays is a very difficult task. This also results in reduced lifetime and more power dissipation. To overcome the above-mentioned drawbacks, an efficient unused integrated circuits detection algorithm (ICDA) for parallel scan architecture is proposed to differentiate the ‘0’ and ‘1’ in a larger SRAM memory array. The proposed architecture avoids the unbalancing of ‘0’ and ‘1’ concentrations in the on-chip SRAM memory array and also optimizes the area required for the memory array. As per simulation results, the proposed method is more efficient in terms of reliability, the detection rate in both used and unused ICs and reduction of power dissipation in comparison to conventional methods such as backscattering side-channel analysis (BSCA) and network attached storage (NAS) algorithm.

Efficient Long-Time Coherent Integration and Detection Algorithm for Radar High-Speed Target Based on the Azimuth Resampling Technology

Efficient Long-Time Coherent Integration and Detection Algorithm for Radar High-Speed Target Based on the Azimuth Resampling Technology

Anti-Theft System for Library

Object detection and tracking could be an immense, vivacious however inconclusive and trending area of computer vision.Due to its immense use in official surveillances, tracking modules applied in security and lots of other’s applications havemade researchers to devise a lot of optimized and specialized methods. For validation purpose live input video will be taken for the same where book will be getting detected and it can be simulated same for real-time through external hardware added. External Hardware will be having RFID integrated to detect if book is issue or not. In the end we see the proper optimized and efficient algorithm for object detection and alert for security. Object Detection is computer vision technique used to detect object and identify its localization. This technique is not only used to identify the location but also to identify which type of object it is. This CV technique is used to detect objects in real time while maintaining the level of accuracy. By bringing some advancement in it, this system can be very helpful for people to keep track of their precious things or devices which are very expensive and need to be protected. Open CV (Open-Source Computer Vision Library) is a library of programming functions mainly aimed at real- time computer vision

YOLOvT: CSPNet-based attention for a lightweight textile defect detection model

Fabric inspection is a crucial process in the textile industry's quality control. Due to the varying structures, textures, geometric features, and spatial distributions of fabric defects, manual fabric inspection is costly and inefficient. Existing fabric defect detection algorithms struggle to strike a balance among efficiency, accuracy, applicability, and deployment costs. In this model, an efficient lightweight fabric defect detection and classification algorithm based on deep convolutional neural networks is proposed. First, the algorithm performs cluster analysis on the fabric defect dataset to ensure that prior boxes better recall objects with fabric defect geometries and spatial characteristics. Next is fusing the convolutional block attention module attention mechanism and Swin Transformer module with the CSPNet structure. This fusion enhances the model's focus on local features and its ability to capture global contextual information without sacrificing the model's inference speed. Moreover, WIoU or Wise-IoU is used as the bounding box loss function of the model, which improves the convergence speed of the bounding box loss and enhances the positioning ability of the model. Finally, the performance of the improved model was validated on a public dataset, showing varying degrees of improvement compared to the baseline model and other state-of-the-art algorithms, meeting the requirements of modern textile processes.

Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images.

Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.