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21 de may. de 2024 · The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques.
21 de may. de 2024 · Recent advances in deep learning have shown promising results in the detection and recognition of general objects. Using a deep neural network model to extract the effective features from a road image is more effective than the conventional traffic sign recognition (TSR) algorithms.
29 de may. de 2024 · In this study, we present an innovative hybrid approach for traffic sign detection in autonomous driving, combining YOLOv8’s real-time detection capabilities with the Segment Anything Model (SAM), enhanced through Visual Prompt Tuning.
This project introduces a novel deep convolutional neural network (CNN) model aimed at enhancing traffic sign recognition for autonomous vehicle technology and road safety.
14 de may. de 2024 · The proposed method focuses on developing a robust semi-pipeline intelligent system to detect and understand text from traffic road signs boards in various weather conditions. For this purpose, a customized YOLOv5s is deployed for initial panel detection.
16 de may. de 2024 · Traffic sign recognition is a crucial method by which autonomous driving systems acquire road information, and is predominantly based on deep neural networks (DNNs). However, the recognition results of DNNs are not always trustworthy for traffic signs subject to abnormal disturbance.
21 de may. de 2024 · Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural ...
