Standard DatasetsCloudy Day City Road Dash Cam Video Dataset
Welcome to the fascinating world of autonomous driving, powered by our rich and diverse datasets. These datasets, meticulously curated and annotated, are the lifeblood of the self-driving car industry, fueling advancements across various domains.
The "Cloudy Day City Road Dash Cam Video Dataset" is crafted to address the challenges autonomous driving systems face in overcast weather conditions. Captured with driving recorders at a resolution exceeding 1920 x 1080 pixels and a frame rate of over 31 fps, this dataset ensures detailed visibility even under the diffused lighting of cloudy skies. It includes bounding boxes and tags for more than 10 object categories commonly encountered in urban settings, such as humans, cars, electric bicycles, vans, and trucks. This dataset aims to refine AI models' ability to navigate and make informed decisions in less-than-ideal weather conditions, enhancing safety and reliability.
Sample
Specification
Dataset ID
MD-Auto-009
Dataset Name
Cloudy Day City Road Dash Cam Video Dataset
Data Type
ImageVolume
About 1k annotated images
Data Collection
Driving Recorders Images. Resolution is over 1920 x 1080 and the number of frames per second of the video is over 31.
Annotation
Bounding Box,Tags
Annotation Notes
Total more than 10 typical object categories, such as human, car,electric bicycle,van,truck etc.
Application Scenarios
Autonomous Driving
Data Collections
Comprising about 1k annotated images, this dataset provides a focused look at city road conditions during cloudy days. The high-resolution videos offer a clear perspective on urban traffic dynamics and the various challenges posed by reduced lighting and potential weather-related obstructions. The annotations facilitate the training of AI systems to accurately detect and respond to a wide range of objects, ensuring effective navigation through urban environments under cloudy conditions.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
Cityscapes Dataset [Learn more]
Focuses on semantic understanding of urban street scenes, featuring semantic, instance-wise, and dense pixel annotations for various classes. It includes 5,000 finely annotated images and 20,000 coarsely annotated images.
Waymo Open Dataset [Learn more]
Offers a high-quality multimodal sensor dataset for autonomous driving extracted from Waymo self-driving vehicles, covering a wide variety of environments and conditions.
nuScenes Dataset [Learn more]
A comprehensive dataset for autonomous driving that enables researchers to study urban driving situations using the full sensor suite of a real self-driving car. The dataset features camera images, lidar sweeps, and detailed map information.
A2D2 Dataset [Learn more]
The Audi Autonomous Driving Dataset (A2D2) offers a large volume of data with various annotations, including semantic segmentation and 3D bounding boxes.
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