Smart City
Training models for automated and efficient urban development
Computer Vision
The ability to recognize and analyze images and videos
Object Detection
Determining the position of an object through bounding box annotation
Transportation
Utilizing machine learning in the automotive industry
2500
photos
4
weeks
Our Partners
CASE DESCRIPTION
Semantic segmentation of driving lanes, pedestrian crossings, signs, and traffic lights using polygons in CVAT
Additional classification of road traffic objects: cars, pedestrians, bicycles
The data is used for training autonomous driving systems and analyzing road conditions
APPLICATION AREAS OF THE DATASET
Road system automation:
Computer vision for automatic recognition of road markings, vehicles, and signs on surveillance cameras
Autonomous vehicle testing:
Computer vision for training autonomous driving algorithms
Road condition assessment:
Classification for analyzing road conditions, detection for identifying defects and damages in road surfaces
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Why
Training Data
- Quality Assurance:
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Enhanced Data Accuracy
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Consistency in Labels
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Reliable Ground Truth
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Mitigation of Annotation Biases
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Cost and Time Efficiency
- Data Security and Confidentiality:
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GDPR Compliance
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Non-disclosure agreement
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Data Encryption
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Multiple data storage options
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Access Controls and Authentication
- Expert Team:
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6 years in industry
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35 top project managers
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40+ languages
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100+ countries
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250k+ assessors
- Flexible and Scalable Solutions:
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24/7 availability of customer service
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100% post payment
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$550 minimum check
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Variable Workload
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Customized Solutions
Team leads project
Anton Tseluiko
Operations manager
Arthur Kazukevich
Python-developer
Daria Yurkevich
Quality Control Manager