NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks

TL;DR

NADS-Net is a lightweight CNN architecture designed for efficient driver and seat belt detection, utilizing FPN backbone and multiple detection heads, validated on a new dataset with diverse conditions.

Contribution

The paper introduces NADS-Net, a nimble CNN architecture tailored for in-vehicle monitoring, improving efficiency over generic pose estimation models.

Findings

NADS-Net achieves high detection accuracy across diverse conditions.

The architecture is more suitable for real-time in-vehicle applications.

Validation on a new dataset demonstrates robustness and effectiveness.

Abstract

A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble and thus more suitable for in-vehicle monitoring tasks compared to other generic pose estimation algorithms. The new architecture, named NADS-Net, utilizes the feature pyramid network (FPN) backbone with multiple detection heads to achieve the optimal performance for driver/passenger state detection tasks. The new architecture is validated on a new data set containing video clips of 100 drivers in 50 driving sessions that are collected for this study. The detection performance is analyzed under different demographic, appearance, and illumination conditions. The results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development and data collection.