A hybrid deep learning approach for driver distraction detection
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 …
traffic accidents. Current studies to detect distraction postures focus on analysing spatial …
Benchmarking deep learning models for driver distraction detection
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 …
of road traffic accidents. Current studies to detect distraction postures focus on analysing …
[HTML][HTML] Automatic driver distraction detection using deep convolutional neural networks
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 …
of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of …
CAT-CapsNet: A convolutional and attention based capsule network to detect the driver's distraction
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 …
driver distraction is identified as one of the main causes. According to the National Highway …
Driver distraction detection using octave-like convolutional neural network
This study proposes a lightweight convolutional neural network with an octave-like
convolution mixed block, called OLCMNet, for detecting driver distraction under a limited …
convolution mixed block, called OLCMNet, for detecting driver distraction under a limited …
[HTML][HTML] Deep learning approach based on residual neural network and SVM classifier for driver's distraction detection
In the last decade, distraction detection of a driver gained a lot of significance due to
increases in the number of accidents. Many solutions, such as feature based, statistical …
increases in the number of accidents. Many solutions, such as feature based, statistical …
[HTML][HTML] EFFNet-CA: an efficient driver distraction detection based on multiscale features extractions and channel attention mechanism
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 …
people obtain serious injuries, and most of them lose their lives. In addition, a continuous …
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 …
economic losses and injuries. Currently, the driver still plays an important role in the driving …
Robust deep learning-based driver distraction detection and classification
Driver distraction is a major cause of road accidents. Distracting activities while driving
include text messaging and talking on the phone. In this paper, we propose a robust driver …
include text messaging and talking on the phone. In this paper, we propose a robust driver …
A deep learning-based driver distraction identification framework over edge cloud
Currently, the number of traffic accidents has been increased globally. One of the main
reasons for this increase is the distraction of the driver on the road. Distracted driving can …
reasons for this increase is the distraction of the driver on the road. Distracted driving can …