scholarly journals Intelligent Drive Assistance System

2021 ◽  
pp. 96-104
Author(s):  
Anuja Kale ◽  
Aditya Raut ◽  
Swati Sinha
Keyword(s):  
Computer Vision ◽  
Motor Vehicle ◽  
Detection System ◽  
Detection Algorithm ◽  
Economic Losses ◽  
Computer Engineering ◽  
Drowsiness Detection ◽  
Work Related ◽  
Physical Injuries ◽  
Major Trend

The report proposes the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver fatigue and sleepiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project. Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system. Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road. A person driving needs to be able to focus on driving at all instances. Any prolonged or sudden complications to the person driving the vehicle can cause serious accidents/damages. To ignore the importance of this could result in severe physical injuries, deaths and economic losses. Road incidents remain the leading type of fatal work-related event, carrying tremendous personal, social, and economic costs. While employers with a fixed worksite can observe and interact directly with workers in an effort to promote safety and reduce risk, employers with workers who operate a motor vehicle as part of their job have fewer options. Drowsiness detection system is regarded as an effective tool to reduce the number of road accidents. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. Haar face detection algorithm is used to capture frames of image as input and then the detected face as output.

scholarly journals A DROWSINESS DETECTION SYSTEM USING COMPUTER VISION AND IoT

IARJSET ◽  
2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Uche M. Chikezie ◽  
Nkolika O. Nwazor (Ph.D.)
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Drowsiness Detection

scholarly journals Drowsiness Detection using CNN

2021 ◽  
Author(s):  
Sheela. S ◽  
Jothika. D. E ◽  
Swarnapriya. N ◽  
Vishali. B. R
Keyword(s):  
Detection System ◽  
Reliable System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Physical Injuries ◽  
Car Accidents ◽  
Current Systems ◽  
Financial Losses ◽  
Shed Light ◽  
Made In

Driver drowsiness has become one of the leading causes of car accidents in recent years resulting in serious physical injuries, fatalities and substantial financial losses. According to statistics, are liable driver drowsiness detection system is needed to warn the driver before a collision occurs. Behavioural, physiological and vehicle based measures are used by researchers to assess driver drowsiness. A thorough examination of these measures will shed light on the current systems, their problems, and the improvements that must be made in order to create a reliable system. By doings other measures will reveal existing systems, their flaws and the changes that must be made in order to produce a credible system.

scholarly journals Driver Drowsiness Detection using MATLAB

2019 ◽  
Vol 8 (2) ◽  
pp. 843-844
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Binary Image ◽  
Detection Function ◽  
Image Processing Techniques ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Face Features ◽  
Processing Techniques

Drowsiness is major cause of accidents. So, this drowsiness detection system alerts the drowsy drivers in order to reduce the risk of potential accidents. The proposed system uses computer vision and image processing technology of MATLAB for detecting the drowsiness. MATLAB detects if eyes are closed or open using various image processing techniques performed using Viola-Jones face features detecting algorithm and skin y,cb,cr values detection function ,converting image into a binary image which was further employed to extract eye characteristics, and its closing frequency, determining drowsiness.

scholarly journals Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (24) ◽  
pp. 11868
Author(s):  
José Naranjo-Torres ◽  
Marco Mora ◽  
Claudio Fredes ◽  
Andres Valenzuela
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Visual Analysis ◽  
Detection System ◽  
Detection Algorithm ◽  
Processing Plant ◽  
Human Beings

Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant,and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

Detection of Sliding Wheels and Hot Bearings Using Wayside Thermal Cameras

2016 ◽  
Author(s):  
Hanieh Deilamsalehy ◽  
Timothy C. Havens ◽  
Pasi Lautala
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Automatic Detection ◽  
Real Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Excessive Heat ◽  
Research Areas

Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

scholarly journals DROWSINESS DETECTION AND MONITORING SYSTEM

2020 ◽  
Vol 32 ◽  
pp. 03045
Author(s):  
Uzair Ghole ◽  
Pravin Chavan ◽  
Siddharth Gandhi ◽  
Rohit Gawde ◽  
Kausar Fakir
Keyword(s):  
Monitoring System ◽  
Detection System ◽  
Human Life ◽  
Drowsiness Detection ◽  
Physical Injuries ◽  
Safety Technology ◽  
Car Safety ◽  
Usb Camera

Wakefulness of a driver is an extremely important factor that needs to be continuously monitored.. A drowsy driver can be a cause of several mishaps and accidents on highways which could lead to loss of money, physical injuries, and the most important, loss of human life. Drowsiness detection system is a car safety technology that helps to prevent and thus reduce accidents caused by the driver getting drowsy. The system is designed for four-wheeler vehicles (or more) wherein the driver’s fatigue or drowsiness is detected and alerts are generated. The proposed method will use a USB camera that captures the driver’s face and eyes and processes the images to detect the driver’s fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method consists of various stages to determine the wakefulness of the driver.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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