Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)

AI-based smart thermal perception systems can cater to the limitations of conventional imaging sensors by providing a more reliable data source in low-lighting conditions and adverse weather conditions. This research evaluates and modifies the state-of-the-art object detection and classifier framewo...

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Autores principales:	Muhammad Ali Farooq, Peter Corcoran, Cosmin Rotariu, Waseem Shariff
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Thermal-infrared object detection advanced driver-assistance systems deep learning edge computing Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a938dbce487c442981ef89c9254f67b1
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id	oai:doaj.org-article:a938dbce487c442981ef89c9254f67b1
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spelling	oai:doaj.org-article:a938dbce487c442981ef89c9254f67b12021-12-02T00:00:21ZObject Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)2169-353610.1109/ACCESS.2021.3129150https://doaj.org/article/a938dbce487c442981ef89c9254f67b12021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9618926/https://doaj.org/toc/2169-3536AI-based smart thermal perception systems can cater to the limitations of conventional imaging sensors by providing a more reliable data source in low-lighting conditions and adverse weather conditions. This research evaluates and modifies the state-of-the-art object detection and classifier framework for thermal vision with seven key object classes in order to provide superior thermal sensing and scene understanding input for advanced driver-assistance systems (ADAS). The networks are trained on public datasets and is validated on test data with three different test approaches which include test-time augmentation, test-time with no augmentation, and test-time with model ensembling. Additionally, a new model ensemble-based inference engine is proposed, and its efficacy is tested on locally gathered novel test data comprising of 20K thermal frames captured with an uncooled LWIR prototype thermal camera in challenging weather and environmental scenarios. The performance analysis of trained models is investigated by computing precision, recall, and mean average precision scores (mAP). Furthermore, the smaller network variant of thermal-YOLO architecture is optimized using TensorRT inference accelerator, which is then deployed on GPU and resource-constrained edge hardware Nvidia Jetson Nano. This is implemented to explicitly reduce the inference time on GPU as well as on Nvidia Jetson Nano to evaluate the feasibility for added real-time onboard installations.Muhammad Ali FarooqPeter CorcoranCosmin RotariuWaseem ShariffIEEEarticleThermal-infraredobject detectionadvanced driver-assistance systemsdeep learningedge computingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156465-156481 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Thermal-infrared object detection advanced driver-assistance systems deep learning edge computing Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Thermal-infrared object detection advanced driver-assistance systems deep learning edge computing Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muhammad Ali Farooq Peter Corcoran Cosmin Rotariu Waseem Shariff Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
description	AI-based smart thermal perception systems can cater to the limitations of conventional imaging sensors by providing a more reliable data source in low-lighting conditions and adverse weather conditions. This research evaluates and modifies the state-of-the-art object detection and classifier framework for thermal vision with seven key object classes in order to provide superior thermal sensing and scene understanding input for advanced driver-assistance systems (ADAS). The networks are trained on public datasets and is validated on test data with three different test approaches which include test-time augmentation, test-time with no augmentation, and test-time with model ensembling. Additionally, a new model ensemble-based inference engine is proposed, and its efficacy is tested on locally gathered novel test data comprising of 20K thermal frames captured with an uncooled LWIR prototype thermal camera in challenging weather and environmental scenarios. The performance analysis of trained models is investigated by computing precision, recall, and mean average precision scores (mAP). Furthermore, the smaller network variant of thermal-YOLO architecture is optimized using TensorRT inference accelerator, which is then deployed on GPU and resource-constrained edge hardware Nvidia Jetson Nano. This is implemented to explicitly reduce the inference time on GPU as well as on Nvidia Jetson Nano to evaluate the feasibility for added real-time onboard installations.
format	article
author	Muhammad Ali Farooq Peter Corcoran Cosmin Rotariu Waseem Shariff
author_facet	Muhammad Ali Farooq Peter Corcoran Cosmin Rotariu Waseem Shariff
author_sort	Muhammad Ali Farooq
title	Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
title_short	Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
title_full	Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
title_fullStr	Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
title_full_unstemmed	Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)
title_sort	object detection in thermal spectrum for advanced driver-assistance systems (adas)
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a938dbce487c442981ef89c9254f67b1
work_keys_str_mv	AT muhammadalifarooq objectdetectioninthermalspectrumforadvanceddriverassistancesystemsadas AT petercorcoran objectdetectioninthermalspectrumforadvanceddriverassistancesystemsadas AT cosminrotariu objectdetectioninthermalspectrumforadvanceddriverassistancesystemsadas AT waseemshariff objectdetectioninthermalspectrumforadvanceddriverassistancesystemsadas
_version_	1718404006651363328

Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)

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