Indian Signboard Image Dataset

Key Features

• Geographical Diversity: Collected by over 2,000 contributors from more than 20 cities across India, ensuring extensive representation of Indian signboards.
• Device Variety: All images are captured using smartphones, adding variability in image quality and resolution, which better simulates real-world data.
• Varied Lighting Conditions: The dataset includes images taken in different times of the day, including daylight, evening, and night shots, as well as in varied weather conditions like rain, fog, and clear skies.
• Diverse Angles and Distances: Signboards are captured from multiple viewpoints, angles, and distances, including occluded and partially visible signs, which further challenges image classification models.
• Traffic Sign Diversity: A wide range of Indian traffic signs are cover, including regulatory, warning, and informational signs from across India, representing both rural and urban settings.
• Environmental Challenges: The images capture traffic signs amidst real-world obstructions like vehicles, pedestrians, and other road elements, contributing to unstructured image sets.

Dataset Structure and Annotations

• Detailed Annotations: The dataset supports both classification and detection, making it suitable for a wide variety of computer vision tasks.
• Multiple Formats: The annotations are available in widely-use formats, including COCO, PASCAL VOC, and YOLO, ensuring compatibility with a broad range of machine learning models and frameworks.
• Multi-Category Labels: Each image can be annotate with multiple categories, enabling the detection of complex sign types within the same image.
• Geo-Tagging: Certain images are geo-tag, making them useful for applications involving geographical localization or region-specific traffic sign recognition.

Applications

The Indian Traffic Signboard Image Dataset can be use in multiple fields such as:

• Autonomous Driving: Developing intelligent navigation systems and improving signboard detection for self-driving cars.
• Smart City Solutions: Enhancing traffic management and safety by recognizing and processing signboards in real-time.
• Localization and Mapping: Supporting GPS and navigation systems by providing geo-tagged images for sign detection and mapping accuracy.
• AI Training: Training AI models for complex, real-world scenarios involving unstructured, varied, and occluded traffic signs.