TY - GEN
T1 - A Comparison of Deep Learning Methods for Inebriation Recognition in Humans
AU - Bhango, Zibusiso
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. Studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.
AB - Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. Studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.
KW - Computer vision
KW - Deep learning
KW - Drunk driving
KW - Inebriation detection
KW - Inebriation recognition
KW - R-CNN
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85130904958&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06427-2_51
DO - 10.1007/978-3-031-06427-2_51
M3 - Conference contribution
AN - SCOPUS:85130904958
SN - 9783031064265
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 610
EP - 620
BT - Image Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
A2 - Sclaroff, Stan
A2 - Distante, Cosimo
A2 - Leo, Marco
A2 - Farinella, Giovanni M.
A2 - Tombari, Federico
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Image Analysis and Processing, ICIAP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -
