YOLOv8-Based Car and Bike Accident Detection

Introduction

This project demonstrates the use of YOLOv8 a state-of-the-art object detection algorithm, to detect car and bike accidents. Accident detection can be instrumental in initiating timely responses and improving road safety.

Overview of YOLOv8

YOLOv8 is the latest YOLO (You Only Look Once) series version known for real-time object detection capabilities. Improvements in YOLOv8 include:

• Enhanced architecture for better accuracy and performance.
• Improved training speed and model efficiency.
• Flexible and easier-to-use APIs.

Project Implementation

The implementation is divided into several stages:

1. Dataset Preparation: The model is trained on a dataset containing images of car and bike accidents, annotated for object detection. (Roboflow is used for RAW dataset)
2. Model Configuration: YOLOv8's configuration is adjusted to optimize for accident detection. Pre-trained weights can be utilized to reduce training time and enhance detection accuracy.
3. Training and Validation: The model is trained using a suitable loss function and hyperparameters, followed by validation on a separate dataset to fine-tune its performance.
4. Inference and Testing: Once trained, the model performs inference on test images or real-time Photos to detect and classify accidents.

Implementation Steps:

• Installation and Environment Setup: Install necessary libraries, including Ultralytics' YOLO package and other dependencies.
• Model Training: Utilize pre-annotated accident datasets or create custom annotations for training.
• Model Inference: Run the trained model on test images/videos to detect real-time accidents.