The paper presents a voice-assisted real-time traffic sign recognition system that uses a Convolutional Neural Network (CNN) model for detection and recognition, followed by a text-to-speech engine to narrate the detected signs to the driver.
The system functions in two subsystems:
The key highlights of the system include:
The authors experimented with different YOLO network versions and configurations to optimize the detection speed and accuracy. The final model, based on YOLOv4-tiny, achieved a mean average precision of 64.71% at 55 frames per second, enabling real-time performance.
The system was tested on the German Traffic Sign Detection Benchmark (GTSDB) dataset and the Mapillary Traffic Sign Dataset, demonstrating its effectiveness in detecting traffic signs under various environmental and lighting conditions.
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by Mayura Manaw... às arxiv.org 04-12-2024
https://arxiv.org/pdf/2404.07807.pdfPerguntas Mais Profundas