scholarly journals A Novel Approach to Detect Driver Drowsiness and Alcohol Intoxication using Haar Algorithm with Raspberry Pi

2020 ◽  
Vol 3 (9) ◽  
pp. 48-51
Author(s):  
M. Agna Manu ◽  
Dayana Jaijan ◽  
S. N. Nissa ◽  
S. Jesna ◽  
Abin Shukoor ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Alcohol Intoxication ◽  
Drunk Driving ◽  
Research Area ◽  
Processing Technique ◽  
Road Accident ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Driver Drowsiness

Drowsiness in driver and alcohol consumption are the critical cause of road accident and death. Lives of pedestrian and passengers are put to risk as drivers tend to fall asleep and also when the driver is in his drunken state. Detection of driver drowsiness and its indication is an active research area now. There are 3 methods for detection of driver fatigue which includes vehicle-based method, behavioural method, and physiological based method. We adopt behavioural method. This project is aimed towards developing a prototype of drowsiness and alcohol detection system using Haar algorithm with raspberry pi. This project proposes a real time detection of driver’s drowsiness as well as alcohol intoxication and subsequently alerting them. The primary purpose of this drowsiness and alcohol detection system is to develop a system that can reduce the number of accidents from drowsiness and drunk driving of vehicle. It consists of camera which is placed in front of the driver to detect the face. An alcohol sensor which is a gas sensor used to sense the drinking state of driver. Haar algorithm is used for face detection. The results demonstrate the accuracy and robustness of the hybridized of image processing technique. Thus, it can be concluded the proposed approach is an effective solution for a real-time of driver drowsiness and alcohol detection.

Real Time Defects of Beer Bottle Detection System Based on DSP

2008 ◽  
Vol 392-394 ◽  
pp. 414-418 ◽  
Author(s):  
B. Ren ◽  
Tan Cheng Xie ◽  
X. Nan
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Detection System ◽  
Processing System ◽  
Ccd Camera ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Experimental Result ◽  
Detection Techniques ◽  
Video Images

The paper analyses the problem of beer bottles detection techniques on the beer bottles production line, uses digital image processing technique on the beer bottles online defect detection. The paper puts forward the designing ideas of the hardware, developing flow of the software and the algorithm of beer bottles detection. TMSDM642 is used to set up the real-time video processing system of the hardware .The hardware system is mainly composed of three parts: the part of memory, the part of the input and the part of the output. When beer bottles are put into the work area, the video images of the bottle-mouth and bottle-bottom will be gained by the CCD camera, firstly, preprocessing is used to eliminate video image noise. Secondly, the image segmentation algorithm is used to detect defects in video images. Lastly the goal of extracting defects will be accomplished. The experimental result indicated that this system may effectively exam the flaw or the unqualified beer bottles.

REAL-TIME FIRE AND SMOKE DETECTION USING CAMERA SEQUENCE IMAGE IN TUNNEL ENVIRONMENT

2007 ◽  
Vol 04 (04) ◽  
pp. 327-338 ◽  
Author(s):  
BYOUNGMOO LEE ◽  
DONGIL HAN
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Fire Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Smoke Detection ◽  
Forest Environment ◽  
Exact Position

In this paper, we proposed an image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurring in the tunnel, it is necessary to have a system to sense and minimize the incident as fast as possible. However it is impossible for human observation of Closed-Circuit Television (CCTV) in tunnel for 24 h. So if the fire and smoke detection system through image processing can warn a fire, it will be very convenient, and it can be possible to minimize damage even when no one is in front of the monitor. The fire and smoke detection is different from forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so an indigenous algorithm has to be developed. The two algorithms proposed in this paper are able to detect the exact position at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

scholarly journals Driver Drowsiness Detection System using Machine Learning Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 990-993
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Raspberry Pi ◽  
Data Set ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Road crashes are the most common forms of accidents and deaths worldwide, and the significant reasons for these accidents are usually drunken, drowsiness and reckless behaviour of the driver. According to the World Health Organization, road traffic injuries have risen to 1.25 billion worldwide, which makes driver drowsiness detection a major potential area to avert numerous sleep-induced road accidents. This project proposes an idea to detect drowsiness using machine learning algorithms, hence alarming the driver in real-time to prevent a collision. The model uses the Haar Cascade algorithm, along with the OpenCV library to monitor the real-time video of the driver and to detect the eyes of the driver. The system uses the Eye Aspect Ratio (EAR) concept to determine if the eyes are open or closed. We also feed a data-set file consisting of the facial features data-points to train the machine learning algorithm. The model inspects each frame of the video, which helps to recognize the state of the driver. Furthermore, a Raspberry Pi single-board computer, combined with a camera module and an alarm system, facilitates the project to emulate a compact drowsiness detection system suitable for different automobiles.

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

scholarly journals An Image Processing Based Fungus Detection System for Mangoes

2019 ◽  
Vol 9 (1) ◽  
pp. 3493-3497
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Health Problems ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
High Yield ◽  
Data Set ◽  
Different Color ◽  
The Given

Fruits which grow with high yield in many states of India are rich in proteins. But due to addition of excess pesticides and chemicals intake of these fruits lead to serious health problems. It is necessary to identify the presence of chemical in the fruits before consuming it. In this project we have planned to develop an image processing technique to analyze whether the fruit is free from chemicals and fungus. In our paper, we have implemented MATLAB used as well as fungus present in the fruit. We capture the images of the fruit or we use datasets and train the database with different color-based changes that happen after adding chemicals to the fruit. The enhancement process is carried out in the captured image. Then image is segmented to hit the regions with affected spots in the fruit. K-means method is used to carry out the segmentation process. The input image is compared with the given data set for training to identify the images. In this way unhealthy fruits can be identified and the affected spots in the fruit can be detected.

scholarly journals Design and Development of an Intelligent System for Pothole and Hump Identification on Roads

2019 ◽  
Vol 8 (3) ◽  
pp. 5294-5300 ◽  
Keyword(s):  
Image Processing ◽  
Fuel Consumption ◽  
Intelligent System ◽  
Processing Technique ◽  
Simple Solution ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Ultrasonic Sensors ◽  
Design And Development ◽  
Geographical Position

Country’s economy depend on well-maintained roads as they are major means of transportation. It becomes essential to identify pothole and humps in order to avoid accidents and damages to the vehicles that is caused because of distress to drivers and also to save fuel consumption. In this regard, this work presents a simple solution to detect potholes and humps and hence avoid accidents and help drivers. Potholes are detected using Image Processing Technique and Ultrasonic Sensors are used to detect humps. Controlling device used is Raspberry Pi. The system acquires the geographical position of potholes using Wi-Fi and transmits it to authorities to take corrective measures

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

scholarly journals GPU Accelerated real-time Melanoma Detection

2018 ◽  
Vol 7 (3) ◽  
pp. 1208
Author(s):  
Ajai Sunny Joseph ◽  
Elizabeth Isaac
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing Technique ◽  
Video Data ◽  
Image Processing Technique ◽  
Processing Unit ◽  
Central Processing ◽  
Melanoma Detection ◽  
Real Time Analysis ◽  
Graphical Processing

Melanoma is recognized as one of the most dangerous type of skin cancer. A novel method to detect melanoma in real time with the help of Graphical Processing Unit (GPU) is proposed. Existing systems can process medical images and perform a diagnosis based on Image Processing technique and Artificial Intelligence. They are also able to perform video processing with the help of large hardware resources at the backend. This incurs significantly higher costs and space and are complex by both software and hardware. Graphical Processing Units have high processing capabilities compared to a Central Processing Unit of a system. Various approaches were used for implementing real time detection of Melanoma. The results and analysis based on various approaches and the best approach based on our study is discussed in this work. A performance analysis for the approaches on the basis of CPU and GPU environment is also discussed. The proposed system will perform real-time analysis of live medical video data and performs diagnosis. The system when implemented yielded an accuracy of 90.133% which is comparable to existing systems.  

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