Identifying cracks is critical for the monitoring of civil infrastructure. To enhance inspection efficiency, a proposed autonomous crack segmentation and exploration system enables the agent to navigate itself without human operation, and the agent successfully captures more than 85% of cracks in the training dataset and achieves 82% crack coverage in the testing dataset.
Developing an autonomous crack segmentation and exploration system for civil infrastructure
