Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Abstract

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

Decentralized Vehicle-to-Vehicle (V2V) Intelligent and Sustainable Communications for Improving Traffic Safety

“…By sharing information about their position and speed, vehicles can coordinate their movements and avoid jams. This can lead to shorter travel times and less fuel consumption [6,7]. In addition to accident warning and traffic flow improvement, decentralized V2V communication systems can also be used for a variety of other applications, such as: Cooperative collision avoidance, Cooperative adaptive cruise control, Cooperative lane changing, Green light optimal speed advisory, and Vehicle platooning.…”

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

“…It is important to note that computer vision is still an area of current research and is constantly evolving [14][15][16][17][18][19]. Different techniques require reinforcement of system responses to improve results, such as convolutional neural networks [20][21][22] and the use of a bio-inspired retina that allows the creation of filters for the visual preprocessing stage, analogous to the functioning of some biological retinas. This type of filter is applicable to multiple applications such as navigational robots [23], pattern recognition, and improvements in tone mapping, among others.…”