ANALYSIS OF CAMERA-BASED CRACK DETECTION SYSTEM UNDER SHIP’S HULL DURING DRY DOCK

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.