Analyzing the Efficacy of Convolutional Neural Network Architectures for real-time Driver Behavior Detection: An In-depth Study of VGG16, ResNet50, InceptionV3, and Xception

Abstract

Distracted driving is a significant contributor to traffic accidents worldwide, necessitating effective driver behavior monitoring systems. This study investigates the efficacy of state-of-the-art Convolutional Neural Network (CNN) architectures—VGG16, ResNet50, InceptionV3, Xception, and Xception_Customize_Model—for real-time driver behavior detection. The research employs a systematic methodology to evaluate these architectures on parameters such as accuracy, computational efficiency, and generalization ability. Through data preprocessing techniques like image augmentation and normalization, models were trained to classify driver behaviors such as safe driving, texting, talking on the phone, turning, and others. The experimental results highlight that Xception_Customize_Model achieved the highest validation accuracy of 97.07% with a consistently low validation loss, demonstrating superior performance and stability. The Xception model followed closely with competitive accuracy but exhibited occasional fluctuations in validation performance. ResNet50 also performed well, with a validation accuracy of 88.25%, reflecting robust classification capabilities despite requiring more epochs for stabilization. In contrast, InceptionV3, VGG16, and CNN demonstrated lower performance, with validation accuracies not exceeding 70%, largely due to higher training losses and limited generalization ability. The findings of this study contribute to the development of intelligent transportation systems by enhancing real-time detection of driver distractions, thereby promoting road safety. This research provides valuable insights into optimizing CNN architectures for real-world applications, offering a pathway for practitioners to implement scalable, efficient, and accurate driver monitoring solutions.