Autonomous crack segmentation, quantification and sealing.

This thesis aims to advance the current state-of-the-art algorithms on autonomous pavement crack sealing systems to achieve the overarching goal of self-repairing cities. The study proposes improvements in both model-centric and data-centric deep learning methods to improve segmentation models. Then, a hybrid method is developed by combining the strengths of both the shortest and orthogonal projection methods to accurately measure crack width. Finally, a novel framework is used to model the crack sealing problem as the open-loop Travelling Salesman Problem, then an Improved Discrete Grey Wolf Optimizer is designed to leverage the framework to efficiently seal cracks.