Real-time Detection Technique Research Articles

Sign Language Detection

Sign language serves as the primary mode of communication for millions of individuals worldwide with hearing impairments. Despite its importance, the availability of sign language interpretation services remains limited, contributing to significant communication barriers. To address this challenge, we propose the development of a web application leveraging state-of-the-art deep learning techniques for real-time sign language detection and translation. Key words : Sign language recognition, Gesture recognition, Machine learning algorithms, Human-computer interaction, Hand pose estimation, Image processing

Boosting the sensitivity of single photon ionization time-of-flight mass spectrometry using a segmented focus quadrupole-ion guide

Single photon ionization time-of-flight mass spectrometry (SPI-TOF-MS) is a powerful analytical technique for real-time detection of trace VOCs. However, efficient ion transmission within the ionization chamber has always been a challenging issue in SPI-TOF-MS. In this study, a novel ion guide termed the Segmented Focus Quadrupole Ion Guide (SFQ-IG) was introduced for SPI-TOF-MS. The SFQ-IG device consists of 12 printed circuit boards (PCB), each containing four quarter-ring electrodes with inner diameters progressively decreasing from 26 to 4 mm. The simulation results demonstrated that SFQ-IG exhibited superior ion transmission efficiency than both ion funnel (IF) field and direct current-only (DC-only) field. By integrating into a SPI-TOF-MS, this ion guide was optimized in terms of the ionization source pressure, direct current gradient, and radio frequency amplitude. Further comparative experiments demonstrated that the SPI-TOF-MS with the SFQ-IG exhibited higher sensitivity than both the IF field (1.3–7.4 times) and DC-only field (3.5–8.8 times) for the test VOCs. The improvements in limit of detection (LOD) with the SFQ-IG ranged from 1.6 to 5.3 times compared to the DC-only field for the test VOCs. Fabricated using PCB technology, the SFQ-IG is characterized by its cost-effectiveness, compact size, and high transmission efficiency, facilitating its integration into other mass spectrometers.

Quartz Crystal Microbalance as a Holistic Detector for Quantifying Complex Organic Matrices during Liquid Chromatography: 1. Coupling, Characterization, and Validation.

A matrix in highly complex samples can cause adverse effects on the trace analysis of targeted organic compounds. A suitable separation of the target analyte(s) and matrix before the instrumental analysis is often a vital step for which chromatographic cleanup methods remain one of the most frequently used strategies, particularly high-performance liquid chromatography (HPLC). The lack of a simple real-time detection technique that can quantify the entirety of the matrix during this step, especially with gradient solvents, renders optimization of the cleanup challenging. This paper, along with a companion one, explores the possibilities and limitations of quartz crystal microbalance (QCM) dry-mass sensing for quantifying complex organic matrices during gradient HPLC. To this end, this work coupled a QCM and a microfluidic spray dryer with a commercial HPLC system using a flow splitter and developed a calibration and data processing strategy. The system was characterized in terms of detection and quantification limits, with LOD = 4.3-15 mg/L and LOQ = 16-52 mg/L, respectively, for different eluent compositions. Validation of natural organic matter in an environmental sample against offline total organic carbon analysis confirmed the approach's feasibility, with an absolute recovery of 103 ± 10%. Our findings suggest that QCM dry-mass sensing could serve as a valuable tool for analysts routinely employing HPLC cleanup methods, offering potential benefits across various analytical fields.

A Reliable Fiber-Optic Temperature Sensor Based on Fluorescence Intensity Ratio Technique for Real-Time Human Thermal Detection

A Reliable Fiber-Optic Temperature Sensor Based on Fluorescence Intensity Ratio Technique for Real-Time Human Thermal Detection

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

Deep Learning-Based Intelligent Video Surveillance System for Real-Time Motion Detection

The proliferation of surveillance systems demands advanced solutions to effectively monitor and analyze video feeds for security and safety purposes. In this paper, we propose a Deep Learning-Based Intelligent Video Surveillance System (DIVSS) designed to enhance traditional Closed-Circuit Television (CCTV) setups. DIVSS integrates state-of-the-art deep learning techniques for real-time motion detection and object recognition, enabling proactive threat detection and automated response mechanisms. The system utilizes convolutional neural networks (CNNs) for robust feature extraction and classification, enabling accurate identification of objects of interest amidst complex backgrounds and varying[1] lighting conditions. Furthermore, DIVSS incorporates region of interest (ROI) monitoring to focus attention on specific areas within the surveillance footage, optimizing computational resources and improving response times. We evaluate the performance of DIVSS through comprehensive experiments conducted on diverse surveillance scenarios, demonstrating its effectiveness in enhancing security measures and providing actionable[3] insights for surveillance operators. The proposed system represents a significant advancement in video surveillance technology, offering unparalleled capabilities for proactive threat detection and real-time response in dynamic environments. Keywords— Deep learning has transformed video surveillance, enabling real-time motion detection and object recognition using Convolutional Neural Networks (CNNs). This technology provides proactive threat detection, intelligent systems for ROI monitoring, and enhances overall security measures.

The application of laser‑induced fluorescence in oil spill detection.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Implementing Visual Assistant using YOLO and SSD for Visually-Impaired Persons

Artificial Intelligence has been touted as the next big thing that is capable of altering the current landscape of the technological domain. Through the use of Artificial Intelligence and Machine Learning, pioneering work has been undertaken in the area of Visual and Object Detection. In this paper, we undertake the analysis of a Visual Assistant Application for Guiding Visually Impaired Individuals. With recent breakthroughs in computer vision and supervised learning models, the problem at hand has been reduced significantly to the point where new models are easier to build and implement than the already existing models. Different object detection models exist now that provide object tracking and detection with great accuracy. These techniques have been widely used in automating detection tasks in different areas. A few newly discovered detection approaches, such as the YOLO (You Only Look Once) and SSD (Single Shot Detector), have proved to be consistent and quite accurate at detecting objects in real-time. This paper attempts to utilize the combination of these state-of-the-art, real-time object detection techniques to develop a good base model. Paper also implements a 'Visual Assistant' for visually impaired people. The results obtained are improved and superior compared to existing algorithms.

Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer

Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/sec on refining the proposed model for identifying optimal parameters.

Insulation Detection of Electric Vehicles by Using FPGA-Based Recursive-Least-Squares Algorithm

The principal reason for why electric vehicles are required to serve as an alternative to the more widespread gasoline and petroleum-based vehicles used in modern times is due to the use of an environmentally conscious means of transportation or to circumvent the tumultuous economic dealings of the compressed natural gas and petroleum industries. There is a growing daily need for large, high-voltage e-mobilities, mostly driven by anticipated advancements in electric vehicle technology. Consequently, all of the various components of these vehicles must be able to be accommodated within a limited and compact space. The battery is an essential component in e-mobility. The insulation, health monitoring, and problem diagnostics of lithium-ion (Li-ion) batteries are of utmost importance in ensuring these vehicles’ safety and efficient functioning. Real-time and fast insulation detection techniques are required to ensure safety in high-voltage (HV) vehicles and to avoid insulation failure. This paper used the Recursive-Least-Squares (RLS) algorithm because it is computationally efficient for building the insulation detection system. Based on the RLS technique, we proposed field programmable gate array (FPGA)-based algorithms and implemented them using VHDL coding. The FPGA is very fast at detection, and the error is lower. We validated the FPGA results with MATLAB simulation results from the existing literature, and the errors are much less when using FPGAs. An experimental hardware platform was also created to validate the proposed FPGA technique with various motor and resistive loadings on electric vehicles (EVs).

Detecting Brute Force Attacks Using Machine Learning

The importance of identifying network traffic abnormalities in cybersecurity cannot be emphasized enough, particularly considering the growing frequency and complexity of computer network assaults. With the emergence of new Internet-related technology, there is a corresponding increase in complex assaults. A significant difficulty is dictionary-based brute-force assaults (BFA), which need efficient real-time detection and mitigation techniques. This study explores the detection of SSH and FTP brute-force attacks via the use of the primary objective of our study is to use the machine learning methodology for the identification and detection of SSH and FTP brute-force assaults. Employing many classifiers enables a pretty comprehensive examination of the effectiveness of machine learners in spotting brute force attacks on SSH and FTP. Brute-force attacks are a widely used and perilous technique for acquiring usernames and passwords. Utilizing ethical hacking is a commendable method to assess the impact of a brute-force attack. This article explores several defense tactics and methodologies for using brute-force attacks. The advantages and disadvantages of many defense techniques are listed, along with details on the kind of attack that is most straightforward to recognize. we made use of machine learning (ML) classifiers: Naive Bayes (NB), decision Tree (DT), random forest (RF) Logistic Regression (LG), Quadratic Discriminant Analysis (QDA), Stochastic Gradient Descent (SGD), Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP), we determined that the Random Forest (RF) algorithm achieved the highest level with an accuracy of 99.9%.

Smart Eye for Visually Impaired People

Abstract: This paper presents a novel mobile application tailored to enhance the daily experiences of individuals with visual impairments. Leveraging cutting-edge real-time object detection and Optical Character Recognition (OCR) techniques, the app, constructed using the TensorFlow algorithm and the comprehensive Common Objects in Context (COCO) dataset, aims to empower users by providing invaluable assistance through their mobile devices. The application utilizes the device's camera to seamlessly identify objects in real-time via the object detection algorithm, while simultaneously extracting and vocalizing text from captured images through OCR technology. A crucial aspect of the app lies in its commitment to user-friendliness, ensuring a straightforward interface tailored specifically for the needs of visually impaired individuals. The efficacy of the system was rigorously evaluated, focusing on both accuracy and real-time performance. Results conclusively demonstrate the app's effectiveness and efficiency in offering essential support to visually impaired users, marking a significant stride toward fostering independence and ease in their daily lives. Remarkably, this mobile application stands out for its affordability and accessibility, providing a practical and inclusive solution for individuals with visual impairments. By delivering audio feedback about identified objects and recognized text, this innovative app contributes to a more accessible and autonomous lifestyle for its users, contributing positively to the broader landscape of assistive technology for the visually impaired.

An Improved Traffic Sign Recognition and Road Lane Detection for Self-Driving Cars using YOLO-v8

This paper presents a unique way for improving the efficiency and efficacy of self-driving cars through enhanced traffic sign recognition and road lane detection using the deep learning framework. The selected solution demonstrates good performance across a wide range of environmental challenges by thoroughly training the YOLO model on a massive dataset including various lighting factors, weather scenarios, and other aspects. The model's effectiveness at recognising and categorising multiple traffic signs in a range of circumstances, including low-light and severe weather, has been enhanced. Considering YOLOv8's versatility, the model is trained on a wide range of road lane video datasets, ensuring exact vehicle localization between lanes.Deep convolutional neural network (DCNN) based on residual network 50 (resnet) architecture for sign and lane identification, as well as you only look once (YOLOv8), an advanced CNN technique for real-time object detection, were used to accomplish the proposed model.

Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field

Weed infestation and their management are a critical production challenge in agricultural fields. Palmer amaranth has created management challenges because it has multiple emergence pattern and has evolved resistant to nine unique herbicide sites of action. Effective Palmer amaranth detection and positive identification in field conditions will help to improve Palmer amaranth control. A field-based hyperspectral imaging system was developed to record Palmer amaranth in soybean fields. The data were pre-processed applying Savitzky-Golay 2nd derivative, Multiplicative Scatter Correction, and Standard Normal Variate in a forward feed manner. Recursive feature elimination, SelectFromModel, sequential forward selection, and backward elimination were used to select significant wavebands from the available 224 bands. Later, supervised machine-learning models were generated to classify soybean and Palmer amaranth using the selected wavebands. Matthew’s correlation coefficient (MCC), F1 score, precision, and recall were considered as the most significant parameters to evaluate the models’ performance. The highest result was obtained by quadratic discriminant analysis with a prediction accuracy of 93.95%, a precision of 90.30%, a recall of 90.29%, an F1 score of 0.95, and an MCC score of 0.85. The findings of this study showed that the combination of hyperspectral imaging and machine-learning is a potential technique for real-time weed detection in the open field condition.

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity.

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm's resilience under varying temperatures.

Real-Time Object Detection Overview: Advancements, Challenges, and Applications

Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, YOLO, and Faster R-CNN excel in accuracy and speed. They employ anchor boxes, feature pyramid networks, and non-maximum suppression to balance precision and processing speed. Hardware accelerators like GPUs, TPUs, and FPGAs facilitate real-time inference.
 Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real-time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. The continuous evolution of deep learning algorithms and hardware capabilities pushes the boundaries of this field, making it a dynamic research domain in artificial intelligence.

AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks

With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.

Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

The detection of diseases in rice plants is an essential step in ensuring healthy crop growth and maximizing yields. A real-time and accurate plant disease detection technique can assist in the development of mitigation strategies to ensure food security on a large scale and economical rice crop protection. An accurate classification of rice plant diseases using DL and computer vision could create a foundation to achieve a site-specific application of agrochemicals. Image investigation tools are efficient for the early diagnosis of plant diseases and the continuous monitoring of plant health status. This article presents an Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion for Rice Plant Disease Detection and Classification (ESHODL-MFRPDC) technique. The proposed technique employed a DL-based fusion process with a hyperparameter tuning strategy to achieve an improved rice plant disease detection performance. The ESHODL-MFRPDC approach used Bilateral Filtering (BF)-based noise removal and contrast enhancement as a preprocessing step. Furthermore, Mayfly Optimization (MFO) with a Multi-Level Thresholding (MLT) based segmentation process was used to recognize the diseased portions in the leaf image. A fusion of three DL models was used for feature extraction, namely Residual Network (ResNet50), Xception, and NASNet. The Quasi-Recurrent Neural Network (QRNN) was used for the recognition of rice plant diseases, and its hyperparameters were set using the ESHO method. The performance of the ESHODL-MFRPDC method was validated using the rice leaf disease dataset from the UCI database. An extensive comparison study demonstrated the promising performance of the proposed method over others.

CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images

Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms.

Real-time tool detection in smart manufacturing using You-Only-Look-Once (YOLO)v5

Computer vision plays an essential role in Industry 4.0 by enabling machinery to perceive, analyze, and control production processes. Object detection, a computer vision technique that accurately classifies and localizes objects within images, has gained significant interest. This technique can be applied in various domains, including manufacturing, to assist in the detection of different tools. In this paper, You-Only-Look-Once (YOLO)v5 real-time object detection technique has been developed and optimized, to detect different tool types and their locations in a manufacturing setting. To train the neural network, a dataset of 3,286 tool images from the internet has been collected and annotated. To enhance the model's ability in generalization, three augmented variants of each image have been created to improve rotation invariance. The model's training scheme has been further optimized with stochastic gradient descent after configuring different hyperparameters such as learning rate and momentum. The fine-tuned model achieved a mean average accuracy of 98.3 %, demonstrating the high precision of the model in detecting different tool types and their locations in real-time.