Abstract:
Significant technological breakthroughs in monitoring and maintenance are
being made in the marine industry to guarantee the integrity and safety of
vessels. The goal of this project is to design and develop a camera detection
system for detecting the presence of cracks under the ship’s hull at the dry
dock, to test the algorithm to detect the crack that may be presence under
the ship’s hull and to analyse the effectiveness of the camera detection
system.
The suggested method uses cutting-edge computer vision algorithms by
using YOLO model to examine high-resolution photos taken by cameras,
aiming to identify, and measuring fractures, a crucial structural integrity issue
for ships. By recognizing and classifying different types of hull cracks, the
methodology integrates machine learning methods and computer vision
techniques. The system is made to work in dry dock environments,
considering issues like surface reflections, changing illumination, and the
requirement for real-time analysis. By offering an effective and dependable
instrument for the early detection of cracks, reducing the possibility of
structural failures, and guaranteeing the general safety of marine operations,
the project seeks to improve ship maintenance procedures. The results of
this study could completely change dry dock inspection procedures by
providing the marine sector with an innovative, affordable, and high-tech
proactive hull maintenance solution.
