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    • Home
    • Register
    • Program
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    • 2024 Student Competition
    • DSPS22
      • 2022 Workshop
      • 2022 Program
      • 2022 Presentations
      • 2022 Student Competition
      • 2022 Committees
    • DSPS23
      • 2023 Student Competition
      • 2023 Program
      • 2023 Presentations
  • Home
  • Register
  • Program
  • 2024 Committees
  • 2024 Student Competition
  • DSPS22
    • 2022 Workshop
    • 2022 Program
    • 2022 Presentations
    • 2022 Student Competition
    • 2022 Committees
  • DSPS23
    • 2023 Student Competition
    • 2023 Program
    • 2023 Presentations

recordings

Congratulations to our winners!


Importance of Data Augmentation in Pavement Distress Detection using YOLOv5 


Pavement Defects Detection based on YOLOv5 and Generative Adversarial Network 


Data-Centric Modelling to Improve the Prediction of Pavement Condition 


student data competition

THE STUDENT DATA COMPETITION HAS ENDED. 

THANK YOU TO ALL PARTICIPANTS! 


This is the first DSPS student competition on the application of AI for pavement condition monitoring. The competition will follow a data-centric model instead of the traditional model-centric approaches. Top-down views of pavement image data containing seven main distress types annotated with bounding boxes and polygons will be provided. Participants will systematically change/enhance datasets provided using various data cleaning, annotation, augmentation strategies to improve the accuracy of a predefined model architecture.  


Below you will find full competition information 

and team registration form.

go to student data competition site

Did you miss the Student Data Competition review?

Click here to view

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