Road Sign Detection Computer Vision Project

**Road Sign Detection: Project Overview **

The Road Sign Detection project aims to develop a robust and accurate machine learning model for detecting and classifying road signs in real-time, using advanced computer vision techniques. This project serves as a critical component in the development of autonomous driving systems, intelligent transportation, and driver-assistance technologies, enhancing road safety by reliably identifying road signs under diverse conditions.

**Project Objectives **

Detection and Classification: Detect the presence of road signs in images or video frames and classify them accurately according to specific sign categories. Real-Time Performance: Optimize the model to achieve real-time inference speeds suitable for deployment in systems where latency is critical, such as autonomous vehicles or traffic monitoring systems. Generalization Across Environments: Ensure high performance across varied lighting, weather, and geographical conditions by training on a diverse dataset of annotated road signs. Classes and Tags This project involves multiple classes of road signs, which may include, but are not limited to:

• Stop Signs
• Speed Limit Signs
• Yield Signs
• Pedestrian Crossings
• No Entry Signs
• Warning Signs Each class represents a distinct type of road sign with specific implications for vehicle behavior and road safety.

Data Collection and Annotation

Dataset Size: 739 annotated images. Data Annotation: Each image has been manually annotated to include precise bounding boxes around each road sign, ensuring high-quality training data. Data Diversity: The dataset includes images taken from various perspectives, in different lighting conditions, and at varying levels of image clarity to improve the model's robustness. Current Status and Timeline Data Collection and Annotation: Completed. Model Training: Ongoing, with initial results demonstrating promising accuracy in detecting and classifying road signs. Deployment: Plans are underway to deploy the model on edge devices, making it suitable for use in real-world applications where immediate response times are critical. Project Timeline: The project is set to complete the final stages of training and optimization within the next two months, with active testing and iterative improvements ongoing. External Resources Project on Roboflow Universe: View Project on Roboflow Universe Documentation and API Reference: Detailed documentation on the dataset structure, model training parameters, and deployment options can be accessed within the Roboflow workspace. Contribution and Labeling Guidelines Contributors are welcome to expand the dataset by labeling additional road sign images and diversifying annotations. To maintain consistency:

Labeling Standards: Use bounding boxes to tightly enclose each road sign, ensuring no extra space or missing parts. Quality Control: Annotated images should be reviewed for accuracy, clarity, and proper categorization according to the predefined class types. This Road Sign Detection project is publicly listed on Roboflow Universe, where users and collaborators can download, contribute to, or learn more about the dataset and model performance.