Standard DatasetsLow lighting 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 "Low Lighting Dash Cam Video Dataset" is tailored for autonomous driving systems to navigate through low-light conditions, a crucial capability for safe driving during night-time or in poorly lit environments. Captured with driving recorders at resolutions exceeding 1920 x 1080 pixels and a frame rate of more than 30 fps, this dataset focuses on low lighting scenarios across various settings such as crossroads, avenues, and paths. It encompasses bounding boxes and tags for common urban objects like humans, cars, electric bicycles, vans, and trucks, providing a comprehensive view of the challenges faced by autonomous vehicles in reduced visibility.
Sample
Specification
Dataset ID
MD-Auto-011
Dataset Name
Low lighting Dash Cam Video Dataset
Data Type
ImageVolume
About 800 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 30.
Annotation
Bounding Box,Tags
Annotation Notes
Mainly from Low lighting, crossroad,avenues and paths as the main scene. The labels include human, car,electric bicycle,van,truck etc.
Application Scenarios
Autonomous Driving
Data Collections
This dataset comprises about 800 annotated images, offering detailed insights into urban and suburban environments under low light conditions. The high-resolution videos capture the complexity of navigating through darkened streets, highlighting the importance of accurate object detection and situational awareness for autonomous vehicles after dusk. It serves as a pivotal resource for improving the night-time operational capabilities of self-driving cars, ensuring they can safely handle the intricacies of low-light driving.
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.
Related products
Any further information, please contact us.
