Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas research management. One such field, automotive technology, employs enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as part. With ...

Abstract This paper proposes a traffic sign detection algorithm using improved lightweight YOLOv4 in order to solve the problems of large parameters quantity, poor real-time performance, and low accuracy for unmanned driving system. Firstly, we use MobileNetV2 network replace original feature extraction improve speed by reducing number model parameters. Secondly, add an attention mechanism back...

Building intelligent traffic guide systems has been an interesting subject recently. A good system should be able to observe all important visual information to be able to analyze the context of the scene. To do so, signs in general, and traffic signs in particular, are usually taken into account as they contain rich information to these systems. Therefore, many researchers have put an effort o...

Traffic sign recognition is one of the most important tasks in autonomous driving. Camera-based computer vision techniques have been proposed for this task, and various convolutional neural network structures are used validated with multiple open datasets. Recently, novel Transformer-based models achieved state-of-the-art performance, outperforming networks several tasks. In study, our goal to ...