Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection
Faris H. Rizk1, Sofia Arkhstan2, Ahmed Mohamed Zaki1, Mohamed Ahmed Kandel3, S. K. Towfek*1
1 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
2 Department of Computer Systems, South Ural State University, 454080 Chelyabinsk, Russia
3 Department of Architecture, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
Emails: faris.rizk@jcsis.org; sofia.arkhstan@mail.ru; azaki@jcsis.org; CH1800230@dhiet.edu.eg sktowfek@jcsis.org
Abstract
Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications.
Keywords: Traffic detection; Convolutional Neural Networks (CNNs); Waterwheel Plant Algorithm; computer vision; object detection; traffic monitoring systems.