Damage Detection and Localisation using UAV/Drone with Object Detection

Detecting structural damage is a fundamental process for visually assessing the quality of any building, such as a bridge or skyscraper. Unfortunately, access to inspect buildings, especially in the case of very tall buildings, is usually not immediate, and in any case, it is difficult. For this reason, unmanned aerial vehicles and drones are used to access inaccessible places, having the possibility of reaching them, making videos and taking photos which are then analyzed by ground personnel. In this paper, we propose a method for the automatic detection of structural damage from images. The proposed method employs an object detection model, i.e., YOLO, for the automatic localization of structural damages on different types of buildings. In the evaluation of a dataset composed of more than 700 images acquired from unmanned aerial vehicles and drones, the proposed method obtains a precision of 0.897, a recall equal to 0.904, and a mean Average Precision value (with an Intersection over Union greater than 0.5) equal to 0.924, showing the effectiveness of the proposed method for the identification and location of structural damage.