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

• Published in: SN computer science 2024-05, Vol.5 (5), p.605, Article 605
• Main Authors: Vishwakarma, Pramod Kumar, Jain, Nitin
• Format: Article
• Language: English
• Subjects: 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
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-02944-9

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.