Tiny deep learning models for real-time and efficient embedded driver state detection

This publication has 35 references indexed in Scilit:

• Si-CA MobileNet: A lightweight and efficient convolutional neural network for distracted driver detection
  Neurocomputing, 2025
• Driver distraction detection based on adaptive tiny targets and lightweight networks
  Signal Processing: Image Communication, 2025
• A lightweight model for distracted driver detection based on neural architecture search and coordinate attention
  Computers and Electrical Engineering, 2025
• A lightweight model combining convolutional neural network and Transformer for driver distraction recognition
  Engineering Applications of Artificial Intelligence, 2024
• Driver distraction detection using semi-supervised lightweight vision transformer
  Engineering Applications of Artificial Intelligence, 2023
• A comprehensive review on ensemble deep learning: Opportunities and challenges
  Journal of King Saud University Computer and Information Sciences, 2023
• Driver Behaviors Recognizer Based on Light-Weight Convolutional Neural Network Architecture and Attention Mechanism
  IEEE Access, 2022
• Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction
  Reliability Engineering & System Safety, 2019
• Power- and Time-Aware Deep Learning Inference for Mobile Embedded Devices
  IEEE Access, 2019
• ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
  2018