# Drive-Net: Convolutional Network for Driver Distraction Detection

arXiv: https://arxiv.org/abs/2006.12586 · HTML: https://mlsanity.com/paper/2006.12586/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge Transfer](https://mlsanity.com/paper/2302.04527/) (2023)
- [Detection of Distracted Driver using Convolution Neural Network](https://mlsanity.com/paper/2204.03371/) (2022)
- [A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image Modeling](https://mlsanity.com/paper/2306.00543/) (2023)
- [Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection](https://mlsanity.com/paper/2606.02352/) (2026)

## Abstract (arXiv)

To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approaches: a recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the methods on a publicly available database of images acquired under a controlled environment containing about 22425 images manually annotated by an expert. Results show that Drive-Net achieves a detection accuracy of 95%, which is 2% more than the best results obtained on the same database using other methods