Damaged Traffic Signs Dataset

Conditions Captured:

The dataset encapsulates a range of deteriorations and flaws, including but not limited to:

• Discoloration: Signs with faded colors due to prolonged exposure to sunlight or environmental factors.
• Paint Peeling: Signs with paint coming off, making it harder to detect their design elements.
• Scratches and Surface Damage: Signs damaged by vehicles or debris, which obscures the visibility of key information.
• Bending or Warping: Traffic signs that are bent or physically distorted, altering their appearance from the standard design.
• Rust: Older signs suffering from rust, particularly those made from metal.
• Graffiti or Stickers: Signs vandalized with graffiti, stickers, or other forms of defacement.
• Partial Occlusion: Signs partially blocked by objects such as tree branches, poles, or other obstructions.
• Design and Manufacturing Flaws: Some images capture signs with design flaws or imperfections resulting from poor manufacturing.
• Reflections and Glare: Signs affected by reflections from sunlight or vehicle headlights, which create visibility challenges.
• Blurring: Signs captured under motion or with out-of-focus camera settings, simulating real-world challenges in detection.
• Environmental Wear: The dataset also captures signs under various environmental factors such as rain, dirt, and dust.

Additional Information:

• Resolution: Images are captured at various resolutions, ensuring diversity in image quality.
• Weather Conditions: The dataset includes images taken in different weather conditions, such as sunny, rainy, and foggy days, which add further complexity.
• Lighting Conditions: Photographs were taken under a mix of lighting conditions, including bright daylight, evening, and low-light situations.

Applications:

This dataset is an excellent resource for developing and training machine learning models for:

• Traffic sign recognition and detection systems.
• Damage classification for traffic sign maintenance and road safety assessment.
• Autonomous vehicle navigation and obstacle detection.
• Government or municipal agencies for traffic infrastructure analysis.