AI-enabled flaw detection using multi-sensory data fusion

Abstract

This thesis focuses on the challenge of automated flaw detection by developing a method for surface crack detection that uses a data fusion approach. Flaw detection is needed to detect damage that could compromise structural integrity, leading to further consequences. Identification of flaws is important to analyse the severity and then take action to rectify any issues. Automated approaches using AI are needed to reduce cost as well as to speed up identification and increase accuracy. The presented research developed a method that followed a multi-stage approach, where data from multiple sensors are fused into a 3D representation with the use of AI models. Then, detection is done on that representation to identify the cracks so that further analysis can be done to determine the crack severity. Research contributions are from both stages. First, data fusion improvements for RGB images were worked on, and a novel method for fusing depth data from RGB stereo and LiDAR data was developed. Then, a method for crack identification from RGB-D data using a novel synthetic data generation method was developed. Evaluation of the contributions was carried out to demonstrate the improvements due to data fusion with comparisons to other methods. The key findings included: metric evaluations showing the developed enhanced image fusion technique improved RGB image quality. The developed novel stereo and LiDAR data fusion method showed lower error than either input method alone. The evaluation of the developed novel crack segmentation method using synthetic data showed that models can be effectively trained in the absence of extensive real-world data.