Description:

The Underpass Image Dataset consists of a diverse and comprehensive collection of over 1,000 high-quality images of underpasses from various cities across the globe. The dataset was collected through crowdsourcing efforts from more than 200 distinct locations, ensuring geographical and environmental diversity. Each image in the dataset has been meticulously reviewed and validated by a team of experts in computer vision at Datacluster Labs, ensuring accuracy and relevance.

This dataset captures underpasses under various conditions, making it particularly useful for urban scene classification, object detection, and road structure analysis. It has broad applicability in autonomous vehicle development, road safety analysis, and infrastructure management. The inclusion of various lighting conditions, distances, and viewpoints provides valuable insights for training machine learning models in real-world scenarios.

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Key Dataset Features

Number of Images: 1,000+ high-resolution images.

Data Collection Method: Crowdsourced from 200+ contributors, ensuring extensive coverage and diversity.

Resolution: Images captured in HD and above, starting from 1920×1080, ensuring high clarity for accurate analysis.

Locations Covered: 200+ underpass locations across urban and rural environments.

Diversity: Images feature a wide range of lighting conditions (daylight, nighttime, shadows), varied distances, multiple angles, and viewpoints.

Capture Devices: Primarily captured using smartphones and other portable devices between 2020 and 2022.

Application: Ideal for training AI systems for underpass identification, scene understanding, infrastructure management, and integration into autonomous driving models.

Additional Dataset Characteristics

Environmental Diversity: The dataset incorporates images taken under diverse environmental conditions such as rain, fog, and bright sunshine, making it a robust tool for AI model development.

Urban vs. Rural: It contains both urban and suburban underpasses, providing a comprehensive view of different structural designs and road environments.

Annotations: Each image is meticulously annotated to enable high-quality scene understanding for autonomous vehicles, enabling models to detect underpasses in real-time while navigating roadways.

Annotation Formats Available

COCO

YOLO

PASCAL-VOC

TFRecord

This dataset is sourced from Kaggle.