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

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