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 dri...
详细信息
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 dri...
详细信息
ISBN:
(数字)9798350387131
ISBN:
(纸本)9798350387148
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's versa-tility 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% 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.
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