The problem Accident Detection Model solves

• Problem Statement

Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

• Research Gap

Lack of real-world data - We trained model for more then 1200 images.
Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
Outdated Versions of previous works - We aer using Latest version of Yolo v8.
Proposed methodology
We are using Yolov8 to train our custom dataset which has been 1200+ images, collected from different platforms.
This model after training with 25 iterations and is ready to detect an accident with a significant probability.

• Model Set-up

Preparing Custom dataset - We have collected over 3200 images. We have annotated all of them individually on roboflow and then downloaded in yolov8 format.
Using Google Collab - Further We have used Google collab to code and train the model for time and space optimization.
Coding - We installed Yolov8 , connected our google drive account, created the data.yaml file containing classes and path for train, val, test images path.
Finally we used the Yolo commands to train our model on the data set of train folder.

• Way Forward

This Model can be implemented in the cameras placed on highway Pol with the help of an IoT device in camera it can be used to report to the nearest control room.
This model can also be transformed into an open source web application using Frameworks like Flask which can be used by anyone to cross check any image to know about if any accident occured in it.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what to do to a image with no accident, after referring to some blogs and taking to some ml engineers i got to know that i should use roboflow because they have an inbuilt function in which you can mark an image null if it dont contain any accident. Thats why i used roboflow.
• I faced problem in knowing the best no. of epochs/iteration for my model which can make my model most accurate. Somehow, after some research i got to know that 150-200 is optimal no. of iteration. Also i had to do a bit of research to know whether to use nano, small or large version of YOLO.