ACCIDENT DETECTION USING DEEP LEARNING
Objective
Deep Learning-based accident detection uses Convolutional Neural Networks to analyze vehicle and roadside camera data. This approach accurately identifies accidents, assesses severity, and aids emergency response prioritization, enhancing road safety.
Abstract
Accidents on roadways continue to be a major concern, causing loss of life and property. Timely detection and response to accidents are critical for reducing their impact. This abstract presents a novel approach for accident detection utilizing Deep Learning techniques. We explore the application of Convolutional Neural Networks (CNNs) to process video and sensor data from vehicles and roadside cameras. The proposed system can identify various types of accidents, including collisions, rollovers, and other critical events, with high accuracy. In addition, it can determine the severity of accidents, aiding in prioritizing emergency responses. By leveraging a large dataset of real-world accidents and near misses, we train the model to recognize patterns and anomalies indicative of accidents. The system demonstrates promising results, providing a foundation for the development of real-time accident detection systems that can enhance road safety and save lives.
Keywords:
CNN, CNN layers, Dataset.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
