Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection
Abstract
1. Introduction
- Identify Shortcomings: highlight the limitations and challenges inherent in current inspection methods for detecting corrosion in maritime ballast tanks.
- Showcase Technology Potential: provide preliminary evidence and practical scenarios that illustrate how HSI, LiDAR, and RGB sensors, when used in tandem, could effectively address these gaps.
- Lay Groundwork for Future Research:employ this manuscript as a platform to initiate more focused and technical research, encouraging further exploration and development of integrated inspection methods.
2. Materials and Methods
2.1. Hyperspectral Camera for Material Characterization
2.1.1. Sensor Specifications
2.1.2. Experimental Setup
2.1.3. Data Acquisition Approach
2.1.4. Rationale for Investigation
2.2. Samples for Testing
- Circular steel sheets were artificially corroded using saltwater exposure for varying durations to create samples with visually distinct levels of surface rust (see Figure 2).
- Steel beams exhibiting naturally occurring corrosion, varying levels of residual coating, and areas of bare metal were sourced from scrapyards. These provided non-homogeneous surfaces characteristic of real-world scenarios (see Figure 3).
- A steel plate was prepared with varying numbers of paint layers applied using commercially available spray paint to test the ability to differentiate coating thicknesses (see Figure 4). Coating layers were controlled and recorded; for this purpose, paint was chosen due to its similarity in thickness with commercial maritime coatings and color-matched to the existing steel plate corrosion samples. Coating layers were measured to be the same thickness.
- An aluminum piece was also coated with varying paint thicknesses for comparative analysis (see Figure 3).
2.3. Methodology for Preliminary Coating Assessment Experiments
2.4. LiDAR Sensor for Spatial Mapping
- Geometric Mapping: Creating accurate 3D models of the complex internal tank structure, essential for understanding the environment and documenting its condition.
- Navigation Aid: Providing spatial awareness for a mobile platform (like a UAV), potentially enabling autonomous or semi-autonomous navigation in the confined GPS-denied environment [34].
- Data Integration Framework: Serving as a common 3D reference frame onto which data from other sensors (like HSI or RGB cameras) can be accurately georeferenced and fused, creating comprehensive multi-modal maps [35].
- Anomaly Detection: While not primary for fine corrosion detection, the high-resolution geometric data might reveal large-scale deformations, buckling, or significant build-up indicative of structural issues or severe corrosion requiring further investigation [28].
Sensor Considerations
3. Results
3.1. Hyperspectral Differentiation of Surface Conditions
3.2. LiDAR-Based 3D Mapping and Data Framework
4. Discussion
4.1. Deployment Platform Considerations: UAV
4.1.1. Platform Requirements
- Stability and Motion Control: HSI sensors require stable controlled motion to capture accurate spectral data without distortions caused by drone vibrations and movements. LiDAR and RGB sensors could probably work in a less sophisticated platform. However, advanced stabilization systems, such as gimbals, are crucial for maintaining data quality within the system as a whole [40].
- Environmental Conditions: The harsh conditions of maritime environments, including high humidity and salt spray, can impact sensor performance and longevity, necessitating robust protective measures [41].
- Data Volume and Processing: HSI generates large volumes of data, presenting challenges for real-time onboard processing and efficient data transmission [42].
- Power Consumption: The significant power requirements of HSI sensors can reduce the total flight time and operational efficiency, highlighting the importance of power management strategies [43].
- Integration with Other Sensors: Seamless integration with other sensors, such as LiDAR and RGB cameras, is essential for effective data fusion but requires precise synchronization [44].
- Distance and Field of View: Maintaining a consistent distance and field of view from surfaces is critical for accurate data collection, especially in confined spaces like ballast tanks [45].
- Lighting Conditions: Ensuring adequate and consistent lighting is essential for capturing high-quality hyperspectral data, particularly in low-light conditions typical of tank interiors [46].
4.1.2. Operational Approach and Automation Potential
- Conducting field trials within actual ballast tanks to assess sensor performance under realistic environmental conditions.
- Developing and validating robust data processing and machine learning algorithms for automated classification and analysis of fused LiDAR and HSI data.
- Addressing the engineering challenges of integrating the required sensor suite onto a UAV platform suitable for confined space operations (balancing payload, agility, endurance, and safety).
- Establishing a reliable workflow for hyperspectral data acquisition that is applicable to diverse environments.
- Performing quantitative comparisons against established non-destructive methods (NDT) methods to benchmark the performance of any proposed system.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Challenge | Description |
---|---|
Confined Spaces | Drones must navigate tight enclosed environments that limit maneuverability and increase the risk of collisions. GPS signal reception is often poor, complicating navigation. |
Lighting Conditions | Poor lighting affects visual data acquisition, necessitating onboard lighting systems, which increase power consumption. Reflective surfaces can create shadows and glare. |
Environmental Conditions | High humidity and saltwater can impact drone operation and sensor functionality, requiring protective measures for electronic components. |
Sensor Integration | Integrating multiple sensors (e.g., LiDAR, HSI, Visual RGB) requires careful calibration and synchronization for accurate data fusion, with constraints on payload capacity. |
Data Processing | High-resolution sensors generate large data volumes, necessitating robust onboard processing or efficient transmission for post-processing. |
Autonomy and Control | Real-time data processing for navigation and obstacle avoidance is complex. Autonomous systems require sophisticated algorithms. |
Power Management | High power requirements reduce flight duration, necessitating energy-efficient designs and potential solutions for battery swapping or recharging. |
Communication Constraints | Signal loss is possible in metallic environments. Reliable communication systems are needed to maintain control and data links. |
Collision Avoidance | Advanced systems are needed for real-time collision avoidance. Sensors must reliably detect obstacles in cluttered environments. |
Maintenance and Reliability | Harsh conditions demand regular maintenance. Reliability is crucial to prevent system failures during inspections. |
Data Security and Privacy | Ensuring compliance with data privacy laws and securing sensitive data collected during operations is necessary. |
Training and Human Factors | Operators require specialized training for effective drone use, particularly in manual or semi-automated modes. |
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Enguita, S.P.; Jiang, J.; Chen, C.-H.; Kovacic, S.; Lebel, R. Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection. Electronics 2025, 14, 3065. https://doi.org/10.3390/electronics14153065
Enguita SP, Jiang J, Chen C-H, Kovacic S, Lebel R. Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection. Electronics. 2025; 14(15):3065. https://doi.org/10.3390/electronics14153065
Chicago/Turabian StyleEnguita, Sergio Pallas, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic, and Richard Lebel. 2025. "Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection" Electronics 14, no. 15: 3065. https://doi.org/10.3390/electronics14153065
APA StyleEnguita, S. P., Jiang, J., Chen, C.-H., Kovacic, S., & Lebel, R. (2025). Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection. Electronics, 14(15), 3065. https://doi.org/10.3390/electronics14153065