Texture Analysis to Enhance Drone-Based Multi-Modal Inspection of Structures

Abstract

The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities.

Keywords

• multi-modal data processing
• unmanned aerial vehicle
• texture segmentation
• remote inspection
• thermography
• thermal image segmentation