# Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge Transfer arXiv: https://arxiv.org/abs/2302.04527 ยท HTML: https://mlsanity.com/paper/2302.04527/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation. - Builds on 29 of its 74 references that are in the map. - First posted on arXiv 9 Feb 2023. ## Placed in - Knowledge distillation / [Detection and dense prediction distillation](https://mlsanity.com/distill/detection-and-dense-prediction-distillation/) ## Builds on (most cited first, 29 of 29) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017) - [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017) - [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013) - [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017) - [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018) - [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020) - [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017) - [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016) - [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018) - [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018) - [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017) - [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017) - [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019) - [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017) - [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019) - [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018) - [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018) - [Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches](https://mlsanity.com/paper/2003.03836/) (2020) - [Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks](https://mlsanity.com/paper/1704.02581/) (2017) - [A Fully Progressive Approach to Single-Image Super-Resolution](https://mlsanity.com/paper/1804.02900/) (2018) - [Real-time Distracted Driver Posture Classification](https://mlsanity.com/paper/1706.09498/) (2017) - [Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition](https://mlsanity.com/paper/2006.11538/) (2020) - [Teacher Guided Architecture Search](https://mlsanity.com/paper/1808.01405/) (2018) - [Drive-Net: Convolutional Network for Driver Distraction Detection](https://mlsanity.com/paper/2006.12586/) (2020) - [A New Dataset and Proposed Convolutional Neural Network Architecture for Classification of American Sign Language Digits](https://mlsanity.com/paper/2011.08927/) (2020) ## Built on by (most cited first, 9 of 9) - [Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos](https://mlsanity.com/paper/2306.10159/) (2023) - [AFter: Attention-based Fusion Router for RGBT Tracking](https://mlsanity.com/paper/2405.02717/) (2024) - [Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques](https://mlsanity.com/paper/2505.01973/) (2025) - [A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image Modeling](https://mlsanity.com/paper/2306.00543/) (2023) - [Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision](https://mlsanity.com/paper/2310.05202/) (2023) - [PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer](https://mlsanity.com/paper/2312.14577/) (2023) - [DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification](https://mlsanity.com/paper/2409.05587/) (2024) - [Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning](https://mlsanity.com/paper/2411.13181/) (2024) - [A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection](https://mlsanity.com/paper/2501.11758/) (2025) ## Abstract (arXiv) The number of traffic accidents has been continuously increasing in recent years worldwide. Many accidents are caused by distracted drivers, who take their attention away from driving. Motivated by the success of Convolutional Neural Networks (CNNs) in computer vision, many researchers developed CNN-based algorithms to recognize distracted driving from a dashcam and warn the driver against unsafe behaviors. However, current models have too many parameters, which is unfeasible for vehicle-mounted computing. This work proposes a novel knowledge-distillation-based framework to solve this problem. The proposed framework first constructs a high-performance teacher network by progressively strengthening the robustness to illumination changes from shallow to deep layers of a CNN. Then, the teacher network is used to guide the architecture searching process of a student network through knowledge distillation. After that, we use the teacher network again to transfer knowledge to the student network by knowledge distillation. Experimental results on the Statefarm Distracted Driver Detection Dataset and AUC Distracted Driver Dataset show that the proposed approach is highly effective for recognizing distracted driving behaviors from photos: (1) the teacher network's accuracy surpasses the previous best accuracy; (2) the student network achieves very high accuracy with only 0.42M parameters (around 55% of the previous most lightweight model). Furthermore, the student network architecture can be extended to a spatial-temporal 3D CNN for recognizing distracted driving from video clips. The 3D student network largely surpasses the previous best accuracy with only 2.03M parameters on the Drive&Act Dataset. The source code is available at https://github.com/Dichao-Liu/Lightweight_Distracted_Driver_Recognition_with_Distillation-Based_NAS_and_Knowledge_Transfer.