Omkar2118/datasets huggingface dataset:
Simulations

Simulations

Introducing paper: The Sustainable Future is now: a dynamic model to advance investments in PV and Energy Storage
Damaged Traffic Signs Dataset

Description:

<a href="https://gts.ai/dataset-download/damaged-traffic-signs-dataset/" target="_blank">👉 Download the dataset here</a>

The Damaged Traffic Signs Dataset focuses on Portuguese traffic signs captured under a variety of conditions, providing a comprehensive collection of images showcasing signs in both their regular state and with various forms of damage or deterioration. The dataset is designed to support research and development efforts in the areas of traffic sign detection, classification, and damage recognition for AI and machine learning models.

Dataset Features:

This dataset includes traffic signs that have undergone several types of wear and tear, presenting realistic conditions encountered on the road. The goal is to offer a broad set of visual challenges for training models that can accurately identify and classify traffic signs, even in less-than-ideal circumstances.

Download 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.

This dataset is sourced from Kaggle.