Levee-Crack Detection from Satellite or Drone Imagery Using Machine Learning Approaches

Abstract

This paper describes and compares different approaches for detecting cracks in the concrete toe, other general areas of levees and dams using satellite or drone images. The dataset was sourced from real drone flight data and manually collected and annotated as needed. We compare old and modern algorithms alike to determine which ones perform best in this case. We also explain the reasonings for a particularly interesting case of the viola jones algorithm, where our calculated accuracy is 100%. We study stacking (85% accuracy), and the latest deep learning techniques (90.90% accuracy) as well. This research hopes to help the U.S. Army Engineers Corps integrate the model into drones to better monitor the levee areas prone to disaster.