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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads.

Aayushi Padia¹, Aryan T N¹, Sharan Thummagunti¹

¹Department of DSAI, Indian Institute of Information Technology, Dharwad 580009, India.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This study introduces a lightweight YOLOv8 model for object detection on Indian roads, achieving over 70% accuracy in diverse conditions. It enhances autonomous vehicle safety by addressing unique traffic and weather challenges.

Keywords:

YOLOv8 artificial intelligence (AI)dense traffic intelligent transportation systems (ITSs)intelligent vehicles (IVs)memory efficient scalable sensors weather conditions

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Systems

Background:

Object detection and classification are vital for autonomous vehicle (AV) navigation.
Existing AV algorithms face challenges with unique Indian road conditions like diverse traffic and weather.
There is a need for specialized, efficient algorithms for AVs in India.

Purpose of the Study:

To develop a robust and efficient object detection and classification model for Indian roads.
To adapt deep learning techniques for real-time AV applications in challenging environments.
To improve the safety and reliability of AVs operating in India.

Main Methods:

Utilized the YOLOv8 deep learning model for its lightweight and scalable architecture.
Conducted experimental evaluations using real-life videos from diverse Indian road scenarios.
Assessed performance across 35 distinct object classes, considering factors like low lighting and occlusions.

Main Results:

Achieved a precision of 0.65 for multi-class object detection.
Demonstrated an average real-time accuracy exceeding 70% across various conditions.
Reached a peak accuracy of 95% under optimal conditions, outperforming existing methods.

Conclusions:

The proposed YOLOv8 approach offers a superior balance between model complexity and performance for Indian roads.
The model is well-suited for deployment in AVs due to its high accuracy and minimal computational requirements.
This research contributes to advancing AV technology for complex, real-world driving environments in India.