Design Framework for Road sign and Speed limit Detection Using Deep Learning

Abstract

Road sign and speed limit detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving. This paper presents a design framework for an efficient and robust road sign and speed limit detection system using deep learning techniques. The framework integrates convolutional neural networks (CNNs) and advanced object detection models, such as YOLO (You Only Look Once) or Faster R-CNN, to accurately detect and classify road signs and speed limit indicators in real-time. The system is designed to handle varying environmental conditions, including different lighting, weather, and occlusions. A preprocessing pipeline is employed to enhance image quality and improve detection accuracy. The framework is evaluated on benchmark datasets and real-world road scenarios to assess its performance. Experimental results demonstrate high detection accuracy and computational efficiency, making the proposed framework suitable for deployment in advanced driver-assistance systems (ADAS) and autonomous vehicles. Future improvements include integrating multi-modal sensor fusion and reinforcement learning to enhance robustness and adaptability. This investigate contributes to the improvement of a solid framework for Ethiopian street sign and light discovery distinguishing proof. By leveraging profound learning strategies and optimization approaches, we address the confinements of existing frameworks, such as little datasets and lower acknowledgment precision. The discoveries illustrate the potential of utilizing MobileNet with SGD optimization as an successful arrangement for street sign and light acknowledgment in Ethiopia, clearing the way for progressed street sign and light recognizable proof in robotized self-service hardware.