Posted: April 6th, 2023

Utilizing Drones and Computer Vision for Automated Hull Inspection and Corrosion Mapping

Utilizing Drones and Computer Vision for Automated Hull Inspection and Corrosion Mapping

Corrosion is one of the major challenges faced by the maritime industry, as it affects the structural integrity, performance, and safety of ships. According to a study by NACE International, the global cost of corrosion for the marine sector was estimated at $2.5 trillion in 2016, which is equivalent to 3.4% of the global GDP . Moreover, corrosion can lead to environmental pollution, operational disruptions, and increased maintenance costs.

One of the key factors that influence corrosion is the condition of the hull, which is exposed to various environmental and operational stresses. Therefore, regular hull inspection and corrosion mapping are essential for preventing and mitigating corrosion damage. However, conventional methods of hull inspection and corrosion mapping are time-consuming, labor-intensive, and risky. They typically involve manual or semi-automated techniques that require scaffolding, rope access, or divers to reach the hull surface. These methods are not only costly and inefficient, but also pose safety hazards for the personnel involved.

To overcome these limitations, drones and computer vision have emerged as promising technologies for automated hull inspection and corrosion mapping. Drones are unmanned aerial vehicles (UAVs) that can fly autonomously or remotely controlled by an operator. Computer vision is a branch of artificial intelligence that enables machines to process and analyze visual information. By combining these two technologies, it is possible to create a system that can perform hull inspection and corrosion mapping faster, safer, and more accurately than conventional methods.

How Drones and Computer Vision Work for Hull Inspection and Corrosion Mapping

The basic workflow of using drones and computer vision for hull inspection and corrosion mapping consists of four steps: data acquisition, data processing, data analysis, and data visualization .

– Data acquisition: In this step, drones equipped with cameras and sensors fly over the hull surface and capture high-resolution images and videos. The drones can be programmed to follow a predefined flight path or adapt to the hull geometry using obstacle avoidance algorithms. The drones can also carry other sensors such as ultrasonic thickness gauges or laser scanners to measure the hull thickness or surface profile.
– Data processing: In this step, the images and videos captured by the drones are transferred to a computer system for processing. The computer system applies various computer vision techniques such as image stitching, image enhancement, image segmentation, feature extraction, and object detection to create a digital representation of the hull surface. The computer system can also use machine learning algorithms to classify the images into different categories such as intact, corroded, or coated.
– Data analysis: In this step, the digital representation of the hull surface is analyzed to identify and quantify the extent and severity of corrosion. The computer system can use computer vision techniques such as edge detection, contour detection, shape analysis, texture analysis, color analysis, and pattern recognition to detect and measure corrosion features such as pits, cracks, blisters, rust stains, or coating defects. The computer system can also use machine learning algorithms to estimate the corrosion rate or predict the remaining service life of the hull.
– Data visualization: In this step, the results of the data analysis are presented in a user-friendly format such as maps, charts, graphs, or reports. The data visualization can provide useful information such as the location, size, shape, type, and distribution of corrosion features on the hull surface. The data visualization can also provide recommendations for corrective actions such as cleaning, painting, repairing, or replacing the corroded parts.

Benefits of Using Drones and Computer Vision for Hull Inspection and Corrosion Mapping

Using drones and computer vision for hull inspection and corrosion mapping offers several benefits over conventional methods . Some of these benefits are:

– Reduced time: Drones can cover large areas of the hull surface in a short time compared to manual or semi-automated techniques. For example, a drone can inspect a 300-meter-long ship in less than an hour . Moreover, drones can operate in any weather condition or time of day without affecting the quality of the images.
– Reduced cost: Drones can reduce the cost of hull inspection and corrosion mapping by eliminating the need for scaffolding, rope access, or divers. Drones can also reduce the cost of maintenance by detecting corrosion at an early stage and preventing further deterioration.
– Reduced risk: Drones can reduce the risk of injury or death for the personnel involved in hull inspection and corrosion mapping by avoiding direct contact with the hull surface. Drones can also reduce the risk of environmental damage by minimizing the use of chemicals or abrasive materials for cleaning or coating the hull.
– Increased accuracy: Computer vision can increase the accuracy of hull inspection and corrosion mapping by providing objective and quantitative measurements of corrosion features. Computer vision can also increase the accuracy of corrosion prediction by using advanced machine learning algorithms that learn from historical data and current conditions.

Challenges and Future Directions of Using Drones and Computer Vision for Hull Inspection and Corrosion Mapping

Despite the benefits of using drones and computer vision for hull inspection and corrosion mapping, there are also some challenges and limitations that need to be addressed. Some of these challenges are:

– Regulatory issues: Drones are subject to various regulations and restrictions depending on the country, region, or industry. For example, drones may require permits, licenses, or certifications to operate in certain airspace or maritime zones. Drones may also need to comply with safety, security, privacy, or environmental standards.
– Technical issues: Drones and computer vision are still evolving technologies that face technical challenges such as battery life, communication, navigation, stabilization, calibration, resolution, noise, occlusion, illumination, or distortion. These challenges may affect the performance, reliability, or accuracy of the system.
– Human factors: Drones and computer vision may require human intervention or supervision for tasks such as data validation, quality control, decision making, or action implementation. These tasks may require specialized skills, knowledge, or experience from the operators or users of the system.

To overcome these challenges and improve the system, some possible future directions are:

– Developing new regulations and standards that facilitate the adoption and integration of drones and computer vision in the maritime industry.
– Developing new technologies and methods that enhance the capabilities and functionalities of drones and computer vision for hull inspection and corrosion mapping.
– Developing new training and education programs that prepare and empower the operators and users of drones and computer vision for hull inspection and corrosion mapping.

Conclusion

Drones and computer vision are emerging technologies that have the potential to revolutionize the field of hull inspection and corrosion mapping. By combining these technologies, it is possible to create a system that can perform hull inspection and corrosion mapping faster, safer, and more accurately than conventional methods. However, there are also some challenges and limitations that need to be addressed before the system can be fully implemented and adopted in the maritime industry. Therefore, further research and development are needed to overcome these challenges and improve the system.

References

: NACE International (2016). International Measures of Prevention, Application, and Economics of Corrosion Technologies (IMPACT) Study. Retrieved from https://impact.nace.org/documents/impact-study-web.pdf
: Liu, Y., Liang, X., & Zhang, H. (2019). A Review on UAV-Based Inspection for Corrosion Detection in Offshore Structures. Sensors (Basel), 19(10), 2259. https://doi.org/10.3390/s19102259
: Almeida Junior, A. F., de Souza Brito Junior, J., & de Oliveira e Souza Filho, J. (2018). A Review on UAV-Based Structural Health Monitoring: From Data Acquisition to Data Analysis. Journal of Nondestructive Evaluation , 37(4), 64. https://doi.org/10.1007/s10921-018-0521-8
: Khoramshahi E., Ristea A., Ghabcheloo R., & Mattila J. (2018). A Review on Vision-Based Inspection Systems for Structural Health Monitoring of Ship Hulls. Journal of Marine Science and Engineering , 6(4), 128. https://doi.org/10.3390/jmse6040128
: Kongsberg Maritime (2020). Kongsberg Maritime launches new generation of underwater mapping drone . Retrieved from https://www.kongsberg.com/maritime/about-us/news-and-media/news-archive/2020/kongsberg-maritime-launches-new-generation-of-underwater-mapping-drone/