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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Veröffentlicht in:SN computer science Jg. 5; H. 5; S. 605
Hauptverfasser: Vishwakarma, Pramod Kumar, Jain, Nitin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2024
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-02944-9