EFFNet-CA: an efficient driver distraction detection based on multiscale features extractions and channel attention mechanism

T Khan, G Choi, S Lee - Sensors, 2023 - mdpi.com
Driver distraction is considered a main cause of road accidents, every year, thousands of
people obtain serious injuries, and most of them lose their lives. In addition, a continuous …

Benchmarking deep learning models for driver distraction detection

J Mafeni Mase, P Chapman, GP Figueredo… - … , Optimization, and Data …, 2020 - Springer
Abstract The World Health Organisation reports distracted driving as one of the main causes
of road traffic accidents. Current studies to detect distraction postures focus on analysing …

AB-DLM: an improved deep learning model based on attention mechanism and BiFPN for driver distraction behavior detection

T Li, Y Zhang, Q Li, T Zhang - IEEE Access, 2022 - ieeexplore.ieee.org
Driver distraction behavior causes a large number of traffic accidents every year, resulting in
economic losses and injuries. Currently, the driver still plays an important role in the driving …

Driver distraction detection with a two-stream convolutional neural network

Y Ma, Z Yin, L Nie - 2020 - sae.org
Driver distraction detection is crucial to driving safety when autonomous vehicles are co-
piloted. Recognizing drivers' behaviors that are highly related with distraction from real-time …

Performance comparison of deep cnn models for detecting driver's distraction

According to various worldwide statistics, most car accidents occur solely due to human
error. The person driving a car needs to be alert, especially when travelling through high …

[HTML][HTML] Automatic driver distraction detection using deep convolutional neural networks

MU Hossain, MA Rahman, MM Islam, A Akhter… - Intelligent Systems with …, 2022 - Elsevier
Recently, the number of road accidents has been increased worldwide due to the distraction
of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of …

A lightweight model combining convolutional neural network and Transformer for driver distraction recognition

X Tang, Y Chen, Y Ma, W Yang, H Zhou… - … Applications of Artificial …, 2024 - Elsevier
Driver distraction recognition has been studied by many researchers. However, most studies
have failed to balance the efficiency and accuracy of models. In this study, a lightweight …

CAT-CapsNet: A convolutional and attention based capsule network to detect the driver's distraction

H Mittal, B Verma - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Worldwide inflation in the count of road accidents has raised an alarming scenario wherein
driver distraction is identified as one of the main causes. According to the National Highway …

A hybrid deep learning approach for driver distraction detection

JM Mase, P Chapman, GP Figueredo… - … on information and …, 2020 - ieeexplore.ieee.org
The World Health Organisation reports distracted driving actions as the main cause of road
traffic accidents. Current studies to detect distraction postures focus on analysing spatial …

[PDF][PDF] Detecting Driver Distraction Using Deep-Learning Approach.

KA Al Shalfan, M Zakariah - Computers, Materials & Continua, 2021 - cdn.techscience.cn
Currently, distracted driving is among the most important causes of traffic accidents.
Consequently, intelligent vehicle driving systems have become increasingly important …