In recent years, many studies have focused on using deep-learning approaches for automatic defect detection in the thermographic inspection of industrial and construction components. Deep Convolutional Neural Networks have proven to perform remarkably on thermal defect detection. However, their convergence and accuracy are heavily associated with having a large amount of training data to avoid overfitting and ensure reliable detection. Unfortunately, the number of available labeled thermal datasets for inspection-related applications is very limited. One of the practical approaches to address this issue is data augmentation. This paper proposes a novel approach for augmenting simulated thermal defects on regions of interest using coupled thermal and visible images. The visible images are employed to extract regions of interest in both modalities using a texture segmentation method. Later, the introduced method is used to augment thermal defects on thermal images.
Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed.
The conventional methods for inspection of industrial sites involve the revision of data by an experienced inspector during the acquisition process to avoid possible data missing and misinterpretation. Despite all the advantages of drone-based inspection, inspectors often do not easily have physical access to the site to check for any data ambiguity. Therefore, it is essential for autonomous or semi-autonomous systems to check for missing data or to highlight possible data ambiguity. Reflection in thermal imagery data is one of the main sources of misinterpretation, and it can be problematic when there is no physical access to the site for a secondary inspection. In this paper, we present a novel algorithm based on the analysis and stitching of consecutive aerial thermal images to detect areas with reflection effect and possibly reduce these effects. The conducted experiments have shown significant results in the detection of reflection in drone-based thermographic inspections.
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