| KEYWORD |
Computer Vision (YOLOv5) and Python for image-based damage detection in bridges
Thesis abroad
keywords COMPUTER VISION, DAMAGE DETECTION, INSPECTION, MACHINE LEARNING, PYTHON
Reference persons MARCO CIVERA, CECILIA SURACE
External reference persons Prof Alessandro Sabato, University of Massachusetts Lowell
Thesis type RESEARCH / EXPERIMENTAL
Description The proposed M.Sc. project aims to leverage Computer Vision techniques, specifically utilizing the YOLOv5 model, combined with Python programming, to develop a sophisticated system for detecting damage in bridges through image analysis. By integrating YOLOv5, a state-of-the-art object detection algorithm, with Python's versatility, the project seeks to automate the process of identifying structural issues such as cracks, corrosion, or other visible surface damage in bridge infrastructure. This innovative approach promises to enhance inspection efficiency, accuracy, and safety, ultimately contributing to the timely maintenance and preservation of these critical infrastructure assets.
The end result will be a 3D model like the one already developed for civil buildings (see attached picture).
The M.Sc. thesis will include laboratory and field tests (in situ) on real-size bridges in Massachussets, USA.
See also image20240229152202.png
Required skills MATLAB, possibly prior knowledge of Machine Learning algorithms
Notes Average grade required: >= 27/30 and knowledge of the English language at least B2/C1 level
Deadline 28/03/2025
PROPONI LA TUA CANDIDATURA