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USE CASE

Roads Segmentation Dataset

Road Path Segmentation with Dashcams
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

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

FACTORS AFFECTING THE FINAL PROJECT COST INCLUDE

Our data quality guarantee is 95%. For annotation orders requiring quality above 95%, we offer enterprise solutions

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

Team leads project

Anton Tseluiko
Operations manager
Arthur Kazukevich
Python-developer
Daria Yurkevich
Quality Control Manager
woman

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