Design and Augmentation of a Deep Learning Based Vehicle Detection Model for Low Light Intensity Conditions

The development of autonomous vehicles and the Advanced Driver Assistance System (ADAS) has accelerated recently, effective traffic management and road safety depend heavily on vehicle identification. However, reliable vehicle detection in low-light situations at night or in bad weather remains a ch...

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Vydáno v:	SN computer science Ročník 5; číslo 5; s. 605
Hlavní autoři:	Vishwakarma, Pramod Kumar, Jain, Nitin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.06.2024 Springer Nature B.V
Témata:	Advanced driver assistance systems Algorithms Artificial neural networks Classification Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data analysis Data augmentation Data Structures and Information Theory Datasets Deep learning Information Systems and Communication Service Labeling Luminous intensity Machine learning Neural networks Object recognition Original Research Pattern Recognition and Graphics Roads & highways Safety management Security for Communication and Computing Application Semantics Software Engineering/Programming and Operating Systems Traffic management Traffic safety Vehicle identification Vehicles Vision Vision systems Deep learning CNN Algorithms Evolution metrics Object detection YOLOv8
ISSN:	2661-8907, 2662-995X, 2661-8907
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Abstract	The development of autonomous vehicles and the Advanced Driver Assistance System (ADAS) has accelerated recently, effective traffic management and road safety depend heavily on vehicle identification. However, reliable vehicle detection in low-light situations at night or in bad weather remains a chronic difficulty in real-world scenarios. This study aims to meet the urgent requirement for enhanced vehicle detection in low light circumstances by developing and enhancing a deep learning-based model. An alternative method is suggested that integrates cutting-edge Convolutional Neural Networks (CNNs) with inventive data augmentation approaches designed specifically for low-light situations. Most object detection models don’t perform efficiently under low-light conditions and lack enlightenment conditions, due to inappropriate labeling. When objects have a small number of pixels and the presence of simple elements is rare, conventional CNNs might have detrimental effects on accurate data analysis due to the excessive amount of convolutional operations. This study introduces information assortment and the labeling of low-light information to deal with different kinds of circumstances for vehicle detection. Besides, this work proposes an explicitly upgraded model dependent on the YOLO model.
AbstractList	The development of autonomous vehicles and the Advanced Driver Assistance System (ADAS) has accelerated recently, effective traffic management and road safety depend heavily on vehicle identification. However, reliable vehicle detection in low-light situations at night or in bad weather remains a chronic difficulty in real-world scenarios. This study aims to meet the urgent requirement for enhanced vehicle detection in low light circumstances by developing and enhancing a deep learning-based model. An alternative method is suggested that integrates cutting-edge Convolutional Neural Networks (CNNs) with inventive data augmentation approaches designed specifically for low-light situations. Most object detection models don’t perform efficiently under low-light conditions and lack enlightenment conditions, due to inappropriate labeling. When objects have a small number of pixels and the presence of simple elements is rare, conventional CNNs might have detrimental effects on accurate data analysis due to the excessive amount of convolutional operations. This study introduces information assortment and the labeling of low-light information to deal with different kinds of circumstances for vehicle detection. Besides, this work proposes an explicitly upgraded model dependent on the YOLO model.
ArticleNumber	605
Author	Vishwakarma, Pramod Kumar Jain, Nitin
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SubjectTerms	Advanced driver assistance systems Algorithms Artificial neural networks Classification Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data analysis Data augmentation Data Structures and Information Theory Datasets Deep learning Information Systems and Communication Service Labeling Luminous intensity Machine learning Neural networks Object recognition Original Research Pattern Recognition and Graphics Roads & highways Safety management Security for Communication and Computing Application Semantics Software Engineering/Programming and Operating Systems Traffic management Traffic safety Vehicle identification Vehicles Vision Vision systems
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Title	Design and Augmentation of a Deep Learning Based Vehicle Detection Model for Low Light Intensity Conditions
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