Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of auto...

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Autores principales:	Mohammad Junaid, Zsolt Szalay, Árpád Török
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Mask R-CNN transfer learning inverse gamma correction illumination instance segmentation pedestrian custom dataset Technology T
Acceso en línea:	https://doaj.org/article/f1f9e9c877b7479cb9d8fb39972ec0c9
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id	oai:doaj.org-article:f1f9e9c877b7479cb9d8fb39972ec0c9
record_format	dspace
spelling	oai:doaj.org-article:f1f9e9c877b7479cb9d8fb39972ec0c92021-11-11T15:57:03ZEvaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions10.3390/en142171721996-1073https://doaj.org/article/f1f9e9c877b7479cb9d8fb39972ec0c92021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7172https://doaj.org/toc/1996-1073Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.Mohammad JunaidZsolt SzalayÁrpád TörökMDPI AGarticleMask R-CNNtransfer learninginverse gamma correctionilluminationinstance segmentationpedestrian custom datasetTechnologyTENEnergies, Vol 14, Iss 7172, p 7172 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mask R-CNN transfer learning inverse gamma correction illumination instance segmentation pedestrian custom dataset Technology T
spellingShingle	Mask R-CNN transfer learning inverse gamma correction illumination instance segmentation pedestrian custom dataset Technology T Mohammad Junaid Zsolt Szalay Árpád Török Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
description	Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.
format	article
author	Mohammad Junaid Zsolt Szalay Árpád Török
author_facet	Mohammad Junaid Zsolt Szalay Árpád Török
author_sort	Mohammad Junaid
title	Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
title_short	Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
title_full	Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
title_fullStr	Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
title_full_unstemmed	Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
title_sort	evaluation of non-classical decision-making methods in self driving cars: pedestrian detection testing on cluster of images with different luminance conditions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f1f9e9c877b7479cb9d8fb39972ec0c9
work_keys_str_mv	AT mohammadjunaid evaluationofnonclassicaldecisionmakingmethodsinselfdrivingcarspedestriandetectiontestingonclusterofimageswithdifferentluminanceconditions AT zsoltszalay evaluationofnonclassicaldecisionmakingmethodsinselfdrivingcarspedestriandetectiontestingonclusterofimageswithdifferentluminanceconditions AT arpadtorok evaluationofnonclassicaldecisionmakingmethodsinselfdrivingcarspedestriandetectiontestingonclusterofimageswithdifferentluminanceconditions
_version_	1718432577655668736

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

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