Real time traffic sign detection and recognition for autonomous vehicle

Abougarair, Ahmed J.; Elmaryul, Mohammed; Aburakhis, Mohamed

doi:10.15406/iratj.2022.08.00249

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…By detecting and recognizing road signs, drivers can be alerted to speed limits, stop signs, pedestrian crossings, and other important traffic regulations. This approach can decrease the risk of accidents and make the road safer for drivers and pedestrians [6]. Also, through the analysis of the traffic signs, signal-control systems can dynamically adjust timing, leading to a decrease in travel time, reducing congestion, achieving efficient traffic flow, and improving transportation efficiency [7].…”

Section: Introductionmentioning

confidence: 99%

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

Khalifa,

Alayed,

Elbadawy

et al. 2024

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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

Khalifa,

Alayed,

Elbadawy

et al. 2024

Applied Sciences

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“…The second phase entails applying an artificial neural network model to classify the detected image. 3,4 Traffic sign detection and identification has become increasingly challenging in recent years. According to the World Health Organization (WHO), enforcing traffic speed limits helps to decrease traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

IRATJ

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The advancement of technology has made it possible for modern cars to utilize an increasing number of processing systems. Many methods have been developed recently to detect traffic signs using image processing algorithms. This study deals with an experiment to build a CNN model which can classify traffic signs in real-time effectively using OpenCV. The experimentation method involves building a CNN model based on a modified LeNet architecture with four convolutional layers, two max-pooling layers and two dense layers. The model is trained and tested with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Parameter tuning with different combinations of learning rate and epochs is done to improve the model's performance. Later, this model is used to classify the images introduced to the camera in real-time. The graphs depicting the accuracy and loss of the model before and after parameter tuning are presented. Also, an experiment was performed to classify the traffic sign image introduced to the camera by using the CNN model. A high probability score is achieved during the process.

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