This document summarizes a project that aims to develop a software system using computer vision and machine learning to detect whether motorcycle riders in Bangladesh are wearing helmets. It will use a camera to take photos of riders, apply object detection models like YOLO to identify bikes and people, and check if the riders are wearing helmets. If not, it will record the bike's license plate number. The document reviews similar existing works and compares the parameters of this project. It outlines that the project will be implemented in Python using YOLO and OpenCV for real-time object detection and helmet detection from images to help enforce road safety in Bangladesh.
Traffic Violation Detector using Object Detection that helps to detects the vehicle number plate that is violating traffic rules and by that number the admin finds the details of the car owner and send a penalty charge sheet to the owner home.
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
Traffic Violation Detector using Object Detection that helps to detects the vehicle number plate that is violating traffic rules and by that number the admin finds the details of the car owner and send a penalty charge sheet to the owner home.
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
This project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.
After decades of anticipation, practical self-driving cars are here. Drive.ai will deploy a self-driving car service for public use in Texas starting in July.
We can continue pushing self-driving forward by focusing on three key elements: industry-leading AI technology, local partnerships, and people-centric safety.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
This project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.
After decades of anticipation, practical self-driving cars are here. Drive.ai will deploy a self-driving car service for public use in Texas starting in July.
We can continue pushing self-driving forward by focusing on three key elements: industry-leading AI technology, local partnerships, and people-centric safety.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
Implementation security system using motorcycle fingerprint identification an...TELKOMNIKA JOURNAL
A motorcycle security system using fingerprint recognition and Telegram notification is a solution to solve motorcycle safety problems and reduce motorcycle loss cases by using fingerprint sensors attached to the owner’s motorcycle and Telegram application as a monitor connected to the motorcycle. The microcontroller used is the Wemos D1 mini connected to the Telegram application as a communication line between the user and the motorcycle. The sensor used is the fingerprint sensor. There are differences in data obtained from the fingerprint sensor response results and the response of the Telegram notification system by testing fingerprints that have been registered and that are not registered on the fingerprint sensor with the measurement results using the stopwatch. Measurement differences on the fingerprint sensor have errors with an average time dispute of 0.27 seconds. Measurement differences in Telegram notifications have errors with an average dispute time of 1 second. Hygiene conditions on the fingerprint sensor influence the difference in reading duration. If there is fingerprint oil before it, then the reading tends to be slow. The difference in measurements obtained is small enough that it can be used as a reference.
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...ijitcs
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
In this paper, a project is described which is a 2-D
modelled version of a car that will learn how to drive itself. It
will have to figure everything out on its own. In addition, to
achieve that the simulator contains a car running
simultaneously &can be controlled by different control
algorithms - heuristic, reinforcement learning-based, etc. For
each dynamic input, the Reinforcement- Learning modifies
new patterns. Ultimately, Reinforcement Learning helps in
maximizing the reward from every state. In this first Part, we
will implement a Reinforcement-Learning model to build an
AI for Self Driving Car. Project will be focusing on the brain
of the car not any graphics. The car will detect obstacles and
take basic actions. To make autonomous car or self-driving
car a reality, some of the factors to be considered are human
safety and quality of life.
Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance and also detect signs boards. The most frequently used principal approach to detect road boundaries and lanes using vision system on the vehicle. In proposed system, we can implement of lane Object detection and also detect Signs board with the help of AI and Deep learning Technique. Input as Dataset to trained using CNN algorithm and creat beast model. Using these model to classify or detect output. Harshada Annasaheb Nikam | Pranav Ashokrao Survase | Ketan Patole | Mansi Sanjay Gore "Lane Detection and Object Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57393.pdf Paper URL: https://www.ijtsrd.com.com/engineering/information-technology/57393/lane-detection-and-object-detection/harshada-annasaheb-nikam
The current scenario in the world is that almost all people have their own vehicle for conveyance. As, vehicle has become the most integral part of mankind. It travels as a part and parcel of man’s life. Theft happens increasingly at one place or the other, when there is improper surveillance. The safety of vehicles is extremely essential in all areas, especially in public places. In this work, the vehicle tracking and locking system are done in order to satisfy the needs of security of the vehicle. Vehicle theft handling is one of the important crises in today's world. Countries which are densely populated like India finds it very difficult to track the vehicle after theft. This system consist an advanced microprocessor board which is connected to the GPS to get the real time location of the vehicle which is then connected to the mobile application through IoT platform. Mobile application will show the location of the vehicle in a map and also the places surrounding the vehicle by capturing it through camera connected to the system. Not only to identify the theft but also it avoids the theft by having dual switching method both on vehicle and the mobile application connected to the vehicle via Bluetooth. It also gives the alert notification in the mobile if someone takes the vehicle when it is turned on in the mobile.
Automated License Plate detection and Speed estimation of Vehicle Using Machi...ijtsrd
A well ordered traffic management system is required in all types of roads, such as off roads, highways, etc. There has been several laws and speed controlled measures are taken in all places with different perspectives. Also Speed limit may vary from road to road. So there are number of methods has been proposed using computer Vision and machine learning algorithms for object tracking. Here vehicles are recognized and detected from the videos that taken using surveillance camera. The aim is to identification of the vehicles and tracking using Haar Classifier, then determine the speed of the vehicle and Finally Detecting the License plate of the vehicle. Detecting the License plate and vehicle speed using machine learning is tough but beneficial task. For the past few years Convolution Neural Network CNN has been widely used in computer vision for vehicle detection and identification. Dlibs are used to track the multiple objects at the same time. P. Devi Mahalakshmi | Dr. M. Babu "Automated License Plate detection and Speed estimation of Vehicle Using Machine Learning - Haar Classifier Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33395.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/33395/automated-license-plate-detection-and-speed-estimation-of-vehicle-using-machine-learning--haar-classifier-algorithm/p-devi-mahalakshmi
What is public health summary?
Public health is the science of protecting and improving the health of people and their communities. This work is achieved by promoting healthy lifestyles, researching disease and injury prevention, and detecting, preventing and responding to infectious diseases.
health publications :
These “basic six” services were: vital statistics, communicable disease control, environmental sanitation, public health laboratory services, maternal and child health services, and public health education
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
2. Road safety has been an issue in Bangladesh for a long time.
Specially people in mega city like Dhaka are less likely to maintain
fundamental road safety like wearing helmet at the time of bike
riding.
Every year we see a significant number of motor bike accident and
most of the time casualty increases only for not wearing helmet.
Our goal is to make such a Python based project that will help
traffic control system with a view to increasing road safety.
We want to build a software based project that will help traffic
control system to detect whether bike rider wears helmet or not.
Introduction
3. It is about motorcycle riders. Camera will detect if the person is
wearing a helmet or not.
If he or she does not wear helmet then camera will take the
vehicle’s license number plate and store the data for further legal
inquires.
Problem Statement
4. 1. Automatic Hardhat Wearing detection By Jixiu Wu.
Summery :This work tries to automate the monitoring of whether
people are wearing hardhat on the construction sites and identify the
corresponding colors.Detection success rate - 83.89%.
2. AI based helmet detection By Ayaz Saiyed
Summery :can detect the helmets from the images with 99%
accuracy . It is implemented using Opencv, Python,YOLOV3 . The
code can be implemented to detect helmets in real time.
Literature Review
5. Parameters Comaparison of our project with similar
works
Title Real time bike Rider’s
Helmet Detection
Automatic Hardhat
Wearing detection
Color detection Not required Required
Camera angle Riders Backside Not restricted to one
angle . i.e (side,front
,back)
Language Python C++ ,Python
Object Detection and
Model training tool
COCO, Open CV,YOLO Tensor Flow, SSD frame
work,YOLOV3,YOLOv4
6. 1. Jixiu Wu, Nian Cai, Wenjie Chen, Huiheng Wang, Guotian Wang ,
Automatic detection of hardhats worn by construction personnel: A deep
learning approach and benchmark dataset, Automation in Construction,
106(2019), 10.1016/j.autcon.2019.102894.
2. https://github.com/AyazSaiyed/Helmet-Detection
3. https://github.com/wujixiu/helmet-detection
References
7. Will be implemented by Python.
• Yolo-Real time object detection for bike, car and person.
• Coco-Object detection by Dataset.
• OpenCV - Library for machine learning.
Application
