YOLO v3 Enhanced for Concrete Disease Detection - 17/03/2024 22:01 EDT
Budget: $30 – $250 USD
Having recognized the need for higher precision detection, we're steering efforts towards augmenting the existing YOLO v3 algorithm. The primary purpose of this project revolves around:
- Increasing the accuracy of disease detection in concrete bridges.
- Achieving enhanced performances in complex backgrounds.
- and able to address reviewers comments
Ideal candidates would demonstrate proficiency in the implementation and improvement of YOLO and similar algorithms, with a proven track record of addressing analogous problems. The projected outcome should ensure a higher degree of accuracy, particularly when operating on real-world complex backgrounds.
We look forward to detecting:
- Cracks
- Corrosion
- Spalling, and
- Rebar
Proven experience in the fields of AI, machine learning, and disease detection will be deemed invaluable for the successful execution of this project.
- Increasing the accuracy of disease detection in concrete bridges.
- Achieving enhanced performances in complex backgrounds.
- and able to address reviewers comments
Ideal candidates would demonstrate proficiency in the implementation and improvement of YOLO and similar algorithms, with a proven track record of addressing analogous problems. The projected outcome should ensure a higher degree of accuracy, particularly when operating on real-world complex backgrounds.
We look forward to detecting:
- Cracks
- Corrosion
- Spalling, and
- Rebar
Proven experience in the fields of AI, machine learning, and disease detection will be deemed invaluable for the successful execution of this project.
Related categories:
Python
Matlab and Mathematica
Machine Learning (ML)
Image Processing
Deep Learning