Real-time Detection Algorithm Research Articles

Real Time Anomaly Detection in Network Traffic: A Comparative Analysis of Machine Learning Algorithms

In the constantly changing field of cybersecurity, real-time intrusion detection using machine learning algorithms has become crucial for protecting network infrastructures. This paper presents a comprehensive literature survey focusing on the comparative study of diverse machine learning algorithms employed for anomaly detection in network traffic. The objective is to critically evaluate the effectiveness of various algorithms in identifying and mitigating threats in real-time scenarios. The study delves into the nuances of prominent machine learning models, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and ensemble methods, as they apply to the domain of anomaly detection. Each algorithm is scrutinized based on its ability to adapt to dynamic network behaviors, handle imbalanced datasets, and provide accurate real-time threat assessments. Throughout the survey, key research contributions are analyzed, encompassing methodologies, datasets, and performance metrics. Comparative insights are provided to emphasize the strengths and weaknesses of each algorithm, elucidating their appropriateness for real-time intrusion detection in network traffic. Notably, the examination extends beyond traditional approaches, exploring recent advancements such as deep learning and ensemble techniques. The findings from this comparative study aim to provide practitioners and researchers with valuable insights into selecting the most suitable machine learning algorithm for real-time anomaly detection in the context of network security. By understanding the comparative performance of these algorithms, organizations can make informed decisions to enhance their cybersecurity posture and fortify their defenses against emerging threats.

UNDERSTANDING YOLO: REAL-TIME OBJECT DETECTION EXPLAINED

YOLO (You Only Look Once) is a groundbreaking real-time object detection algorithm known for its speed and accuracy. This article provides an in-depth exploration of YOLO, from its innovative architecture to its practical applications. We discuss its grid-based approach, prediction of bounding boxes and class probabilities, and the use of Non-Max Suppression to refine detections. Additionally, we cover the training process, advantages, and future developments of YOLO, highlighting its significant impact on fields such as autonomous driving, surveillance, and healthcare. Keywords: YOLO, real-time object detection, algorithm, bounding boxes, Non-Max Suppression.

Deteksi Jalan Berlubang Menggunakan Algoritma Yolov5

The city of Sorong, as one of the largest cities in the Southwest Papua region, is facing serious problems due to potholes in various areas. This problem causes the risk of accidents and vehicle damage, disrupts the mobility of city residents, and hinders sustainable infrastructure development. Therefore, we need a system that is efficient and accurate in detecting potholes quickly. In this study, researchers used the "You Only Look Once" (YOLO v5) method to detect potholes in Sorong City. YOLO v5 is a real-time object detection algorithm that has been proven to have high speed and accuracy in recognizing objects. This approach allows for instant detection of potholes as vehicles pass by, so authorities can quickly take the necessary remedial action. This study aims to implement pothole detection technology using YOLO v5, with the hope of increasing efficiency and accuracy in overcoming potholes in Sorong City and improving traffic safety by detecting potholes in real-time. The results of this study obtained the highest training precision, namely 96.6% and the accuracy value of the pothole detection system test.

Federated learning secure model: A framework for malicious clients detection

The Federated Learning Secure Model Repository presents a novel paradigm to ensure the trustworthiness of machine learning models generated through federated learning. It employs advanced algorithms for real-time detection of malicious contributions, supplemented by varied model auditing methods. By leveraging continuous monitoring, the repository guarantees the integrity and security of shared models, thereby mitigating the risks posed by malicious actors. This innovative approach fortifies the collaborative nature of federated learning while safeguarding against potential model poisoning and maintaining the reliability of the shared model ecosystem.

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

Bearing defect detection plays a crucial role in the intelligent production of chemical transmission equipment, where timely identification and handling of defective bearings are essential. However, in practical large-scale industrial production, product surface defects are often complex, diverse, and exhibit significant variations in appearance, posing severe challenges to the discriminative ability and detection efficiency of bearing defect detection algorithms. This paper proposes a real-time bearing surface defect detection algorithm, CAC-YOLOv8, which designs the Channel Attenuation Network (CAN) and Compound Pooling Pyramid Spatial Pyramid Pooling Fast (CPPSPPF) structure. Specifically, the model introduces the Channel Attenuation Network to achieve parallel feature extraction, deep feature processing, and feature fusion under different channel numbers, capturing critical features related to bearing defects and thereby improving the inference speed. Subsequently, based on the concept of overlapped receptive fields, a CPPSPPF structure is constructed, utilizing multiple iterations of max-pooling operations with smaller pooling kernel sizes to prevent information loss while expanding the receptive field, thereby strengthening the capturing ability of features at different scales. The experimental results indicate that the proposed CAC-YOLOv8 bearing surface defect detection algorithm, compared to the YOLOv8 model, achieved a 0.3% improvement in mAP@0.5, reduced model size by 14.4%, and enhanced model inference speed by 33.3%. This enables the CAC-YOLOv8 model to significantly improve the real-time performance of bearing defect detection while maintaining high-precision detection. The performance in practical industrial detection demonstrates that the proposed approach has achieved outstanding results in both speed and accuracy.

Efficient Real time Zynq 7000 FPGA deployment of optimized YOLOv2 deep leaning model for target detection, based on HDL Coder Methodology

Field Programmable Gate Arrays (FPGAs) have garnered significant attention in the development and enhancement of target identification algorithms that employ YOLOv2 models and FPGAs, owing to their adaptability and user-friendliness. The Simulink HDL compiler was utilized to design, simulate, and implement our proposed design. In an effort to rectify this, this paper presents a comprehensive programming and design proposal. The implementation of the YOLOv2 algorithm for real-time vehicle detection on the Xilinx® Zynq-7000 System-on-a-chip is proposed in this work. Real-time testing of the synthesised hardware revealed that it can process Full HD video at a rate of 16 frames per second. On the Xilinx Zynq-7000 SOC, the estimated dynamic power consumption is less than 90 mW. When comparing the results of the proposed work to those of other simulations, it is observed that resource utilization is enhanced by around 204 k (75%) LUT, 305 (12%) DSP, and 224 k (41%) flip-flops at 200 MHz.

AcciSense – A Real-Time Accident Detection and Emergency Response System

The AcciSense project pioneers advanced technology and intelligent algorithms for real-time accident detection and emergency response optimization on highways. Its core objective is swift accident identification and automated hospital notifications to improve road safety and ensure timely medical aid. By integrating these technologies, AcciSense aims to revolutionize highway emergency response, addressing the need for faster and more effective medical services. A standout feature of AcciSense is its automated ambulance dispatch, reducing response times crucial for saving lives during emergencies. Seamless integration with hospital systems enhances response coordination, ensuring prompt medical assistance at accident sites. This approach highlights technology's transformative role in bolstering road safety and emergency response efficiency. AcciSense focus on intelligent algorithms and cutting-edge technology reflects a commitment to innovation and effectiveness in emergency response and road safety challenges. It promises to enhance highway transportation safety through proactive accident detection and technology-driven response coordination, emphasizing technology's critical role in improving emergency services and saving lives on highways.

Detection of focal to bilateral tonic-clonic seizures using a connected shirt.

This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

IntroductionSea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.MethodsTo address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.Results and discussionThe significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

Jellyfish detection algorithm based on multi-gradient flow feature fusion

Jellyfish detection is a challenge in the field of underwater biological identification. Although early works have solved some of the problems of jellyfish detection, there are still shortcomings. Earlier jellyfish datasets were not as extensive, necessitating heavy reliance on data augmentation. Moreover, the detection capability is limited, especially in scenarios involving jellyfish gatherings and occlusions. To overcome these limitations, we establish a high-quality dataset with more jellyfish species and propose a more robust real-time detection algorithm. Our algorithm primarily consists of a multi-gradient flow backbone and a feature fusion module GFPN. Additionally, we have designed a receptive field expansion module SPPFCSPC_G. The entire network employs the FReLU activation function, while the bounding box regression utilizes the WIOU loss function. Our methods demonstrate accuracy and run-time performance in comparison with the state-of-the-art yolo series algorithms. Results show that our algorithm achieves the highest Precision, Recall and mAP50, exceeding the baseline yolov5 by 1.1%, 4.1%, and 4.5%, and outperforming the latest yolov8 by 0.9%, 1.3%, and 2.5%. Importantly, our method effectively addresses aggregation, occlusion, and deformation issues commonly encountered in jellyfish detection.

A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5

During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based on YOLOv5x was proposed, which enhances feature information and improves anchor box regression prediction to accurately detect the position of foam lines. Firstly, in the preprocessing stage, employing a rectangular box to simultaneously label the foam line and the edge of the A/O pool within the same region, enhances the feature information of the foam line. Then, the C3NAM module was proposed, which applies weight sparse penalties to attention modules in the feature extraction section, to enhance the capability of extracting foam line features. Subsequently, a B-SPPCSPC module was proposed to enhance the fusion of shallow and deep feature information, addressing the issue of susceptibility to background interference during foam line detection. Next, the Focal_EIOU was introduced to ameliorate the issue of class imbalance in detection, providing more accurate bounding box predictions. Lastly, optimizing the detection layer scale improves the detection performance for smaller targets. The experimental results demonstrate that the accuracy of this algorithm reaches 98.9%, and the recall reaches 88.1%, with a detection frame rate of 26.2 frames per second, which can meet the actual detection requirements of real-world application scenarios.

Development of a Real-time Force-based Algorithm for Infusion Failure Detection.

Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumps' occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed. We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumps' onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps. The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds. Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifier's performance in a real-world setting.

Research on Surface Defect Detection of Strip Steel Based on Improved YOLOv7.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s.

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Radiological imaging plays a pivotal role in disease diagnosis, but the manual interpretation is prone to errors and time-consuming. This research explores the utilization of deep learning algorithms for real-time disease detection and classification in radiological images, aiming to enhance diagnostic accuracy and efficiency in healthcare settings. Deep learning algorithms offer a promising solution by automating the detection and classification process, potentially reducing diagnosis time and improving patient outcomes. However, deploying deep learning algorithms for real-time disease detection in radiological images presents several challenges. These challenges also consist of the need for diverse and large datasets for model training, addressing class imbalance and data variability, ensuring robustness to noise and artifacts, and interpreting model decisions for clinical validation. To overcome these challenges, this research proposes several methods, including data augmentation techniques for increasing dataset diversity, transfer learning from pre-trained models for leveraging existing knowledge, ensemble learning for combining multiple models for improved performance, and attention mechanisms to focus on relevant image regions. Additionally, techniques for uncertainty estimation and model interpretability are explored to enhance trust and acceptance of automated diagnosis systems in clinical practice. By addressing these challenges and implementing appropriate methods, deep learning algorithms show promise for real-time disease detection and classification in radiological images, offering a transformative approach to medical imaging analysis. Results show an accuracy of 97%, sensitivity and specificity at 97% and 95%, and an F1 score of 96%.

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Radiological imaging plays a pivotal role in disease diagnosis, but the manual interpretation is prone to errors and time-consuming. This research explores the utilization of deep learning algorithms for real-time disease detection and classification in radiological images, aiming to enhance diagnostic accuracy and efficiency in healthcare settings. Deep learning algorithms offer a promising solution by automating the detection and classification process, potentially reducing diagnosis time and improving patient outcomes. However, deploying deep learning algorithms for real-time disease detection in radiological images presents several challenges. These challenges also consist of the need for diverse and large datasets for model training, addressing class imbalance and data variability, ensuring robustness to noise and artifacts, and interpreting model decisions for clinical validation. To overcome these challenges, this research proposes several methods, including data augmentation techniques for increasing dataset diversity, transfer learning from pre-trained models for leveraging existing knowledge, ensemble learning for combining multiple models for improved performance, and attention mechanisms to focus on relevant image regions. Additionally, techniques for uncertainty estimation and model interpretability are explored to enhance trust and acceptance of automated diagnosis systems in clinical practice. By addressing these challenges and implementing appropriate methods, deep learning algorithms show promise for real-time disease detection and classification in radiological images, offering a transformative approach to medical imaging analysis. Results show an accuracy of 97%, sensitivity and specificity at 97% and 95%, and an F1 score of 96%.

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism.

Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model's training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices.

Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis

Gas turbine malfunctions can significantly impact production and safety. This study proposes an intelligent monitoring system for MS5002C gas turbines using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Long Short-Term Memory (LSTM) algorithms for real-time anomaly detection and predictive maintenance. Based on extensive historical data (1985–2021), the system predicts component degradation and calculates failure probabilities. This enables the development of an effective preventive maintenance plan, extending turbine life and optimizing performance.