P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

TL;DR

This paper introduces P-YOLOv8, a lightweight, efficient, and accurate real-time detection model for distracted driving behaviors, suitable for deployment on embedded devices, outperforming traditional models in speed and accuracy.

Contribution

The study presents P-YOLOv8, a novel optimized YOLOv8-based model that achieves high accuracy with minimal size and computational cost for real-time distracted driving detection.

Findings

Achieves 99.46% accuracy on distracted driving dataset

Model size of only 2.84 MB with 1,451,098 parameters

Outperforms traditional deep learning models in speed and efficiency

Abstract

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8 versatility in both object detection and image classification tasks using the Distracted Driver Detection dataset from State Farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1,451,098, due to its innovative architecture. It achieves a high accuracy of 99.46 percent with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving.