Utilizing Drones and Computer Vision for Automated Hull Inspection and Corrosion Mapping
The hull of a ship is exposed to harsh environmental conditions that can cause corrosion and damage over time. Corrosion can reduce the structural integrity, performance, and safety of the ship, as well as increase the fuel consumption and maintenance costs. Therefore, regular inspection and maintenance of the hull are essential to ensure the optimal operation and longevity of the ship.
However, conventional methods of hull inspection and corrosion mapping are labor-intensive, time-consuming, and risky. They usually involve manual inspection by divers or scaffolding, which can be affected by human errors, weather conditions, and safety hazards. Moreover, manual inspection can only provide qualitative information about the corrosion level and location, which may not be sufficient for effective decision making and planning.
To overcome these limitations, drones and computer vision can be utilized to automate the hull inspection and corrosion mapping process. Drones can fly around the ship and capture high-resolution images of the hull surface from different angles and distances. Computer vision can then process the images and extract quantitative information about the corrosion type, extent, and location using advanced algorithms and techniques.
Drones and computer vision offer several advantages over manual methods of hull inspection and corrosion mapping. First, they can reduce the human involvement and risk, as well as the time and cost of the inspection process. Second, they can provide more accurate, consistent, and comprehensive information about the corrosion condition of the hull, which can facilitate better decision making and planning. Third, they can enable continuous monitoring and analysis of the hull condition over time, which can help detect and prevent potential problems before they become serious.
Several studies have demonstrated the feasibility and effectiveness of using drones and computer vision for automated hull inspection and corrosion mapping. For example, Alawadhi et al. (2018) proposed a drone-based system that can automatically detect corrosion patches on ship hulls using image processing techniques. They tested their system on a real ship hull and achieved an accuracy of 92%. Similarly, Wang et al. (2020) developed a computer vision method that can classify different types of corrosion on ship hulls using deep learning models. They evaluated their method on a large dataset of ship hull images and achieved an accuracy of 96%.
In conclusion, drones and computer vision can provide a novel and promising solution for automated hull inspection and corrosion mapping. They can improve the efficiency, accuracy, and safety of the inspection process, as well as provide valuable information for maintenance planning and optimization. However, there are still some challenges and limitations that need to be addressed, such as the reliability and robustness of the drone navigation and communication systems, the quality and consistency of the image data, and the complexity and diversity of the corrosion patterns.
Alawadhi, H., Al-Ali, A., Islam, S., & Al-Ahmad, H. (2018). Drone-based automatic detection system for ship hull inspection using image processing techniques. In 2018 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-6). IEEE.
Wang, Y., Zhang, J., Liang, J., & Zhang, H. (2020). Ship Hull Corrosion Classification Based on Deep Learning Models. IEEE Access, 8, 149881-149890.