TY - GEN
T1 - Machine Learning – Imaging Applications in Transport Systems
T2 - 2023 IEEE International Conference on Electrical, Computer and Energy Technologies (ICECET 2023)
AU - Adams, Adrian
AU - Abu-Mahfouz, Adnan M.
AU - Hancke, Gerhard P. (Jr)
PY - 2023/11
Y1 - 2023/11
N2 - Transport systems are fundamental to supporting economic growth, and the need for smarter, safer, more efficient and climate neutral transport systems continues to grow. Maintenance and operation of transport infrastructure is expensive and has many difficulties. In recent years, the application of machine learning to solve problems has been pursued with varying success rates. Many open problems still remain at this stage. This paper provides a review of deep learning applications in transport systems. Multiple deep learning applications are discussed e.g. railway safety, self-driving cars, pedestrian crossing and traffic light detection. Reviewed papers are evaluated in terms of challenges, contribution, weakness, research gaps. Key research questions for future study are proposed: performance optimization, data set improvement and the need for research on real-time performance on edge devices. © 2023 IEEE.
AB - Transport systems are fundamental to supporting economic growth, and the need for smarter, safer, more efficient and climate neutral transport systems continues to grow. Maintenance and operation of transport infrastructure is expensive and has many difficulties. In recent years, the application of machine learning to solve problems has been pursued with varying success rates. Many open problems still remain at this stage. This paper provides a review of deep learning applications in transport systems. Multiple deep learning applications are discussed e.g. railway safety, self-driving cars, pedestrian crossing and traffic light detection. Reviewed papers are evaluated in terms of challenges, contribution, weakness, research gaps. Key research questions for future study are proposed: performance optimization, data set improvement and the need for research on real-time performance on edge devices. © 2023 IEEE.
KW - deep learning
KW - edge-devices
KW - neural network
KW - object detection
KW - railway safety
KW - self-driving
KW - transport systems
UR - http://www.scopus.com/inward/record.url?scp=85187308419&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85187308419&origin=recordpage
U2 - 10.1109/ICECET58911.2023.10389341
DO - 10.1109/ICECET58911.2023.10389341
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 979-8-3503-2782-3
T3 - International Conference on Electrical, Computer and Energy Technologies, ICECET
BT - 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)
PB - IEEE
Y2 - 16 November 2023 through 17 November 2023
ER -
