Lane Segmentation Image Dataset
#Image Segmentation #Feature Extraction #Computer Vision #Autonomous Driving #Intelligent Transportation #Lane Detection
- 500 records
- 1.5G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
This dataset has several core advantages. Firstly, it achieves over 95% annotation accuracy in terms of data quality, ensuring high recognition consistency and completeness in different environments. Technologically, it introduces deep learning-based annotation methods and employs data augmentation techniques to improve the model's robustness and generalization ability. The application value lies in enhancing the precision of lane detection and segmentation in autonomous driving systems, with performance indicators improving by more than 10% in practical tests. Compared to similar datasets, this dataset covers more diverse scenarios and weather conditions, providing more detailed annotation. It includes rare data, such as lane data under uncommon weather conditions, and supports compatibility and extended applications for other traffic systems.
Dataset Insights
Sample Examples
f7f2a3a9**.jpg|1060*2376|267.16 KB
07655956**.jpg|1920*2560|582.86 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| lane_type | string | Indicates the type of lane in the image, such as solid line, dashed line, double yellow line, etc. |
| weather_condition | string | Weather condition at the time the image was captured, such as sunny, rainy, foggy, etc. |
| road_surface_condition | string | Describes the clarity and condition of the road surface, such as dry, wet, snowy, etc. |
| light_condition | string | Lighting condition when the image was captured, such as daytime, dusk, nighttime, etc. |
| traffic_density | integer | Number of traffic participants in the image, representing the density of traffic. |
| vehicle_presence | boolean | Indicates whether there are vehicles present in the image. |
| pedestrian_presence | boolean | Indicates whether there are pedestrians present in the image. |
| road_type | string | Type of road captured in the image, such as highway, urban street, country road, etc. |
| lane_marking_visibility | integer | Describes the visibility of lane markings in the image, usually rated on a scale from 1 to 10. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What field of research can the lane segmentation image dataset be used for?
- The lane segmentation image dataset is primarily used for research in the field of transportation driving, especially for lane segmentation tasks in autonomous driving systems.
- Which performance aspects can be improved by using the lane segmentation image dataset?
- Using the lane segmentation image dataset can improve the lane segmentation accuracy and overall driving safety of autonomous driving systems.
- Why is lane segmentation important for autonomous driving systems?
- Lane segmentation is crucial in the autonomous driving process because it helps vehicles recognize lane boundaries, ensuring that they stay in the correct lane and avoid lane departure.
- How can the lane segmentation image dataset be used to promote the development of autonomous driving technology?
- Researchers can use the lane segmentation image dataset to train and validate autonomous driving algorithms, improving their performance and reliability under real road conditions.
- What types of image information does the lane segmentation image dataset contain?
- The lane segmentation image dataset primarily contains image information used for recognizing and segmenting driving lanes, which usually display marked lane lines.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Lane Segmentation Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/e19616bb20cdcca6e12c3376d0cead0b?dataset_scene_cate_type=6},
urldate={2026-02-04},
keywords={lane segmentation dataset, autonomous driving image segmentation, traffic sign detection},
version={1.0}
}Using this in research? Please cite us.
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