Detecção e mensuração de fissuras em edificações combinando o uso de drones com visão computacional e aprendizagem de máquina

Abstract

The inspection of structures and buildings is an essential activity to ensure safety, performance, and durability in civil construction. However, traditional methods based on visual inspection present significant limitations, such as difficulty accessing elevated regions, exposure of professionals to risks, and dependence on the inspector’s interpretation. In this context, technologies based on drones, computer vision, and machine learning emerge as alternatives to automate the process and increase the reliability of the analysis. This work proposes a computational method for the automatic detection and measurement of cracks in buildings using images captured by drones. The method consists of two stages: the first involves automatic crack detection using a model of convolutional neural network, trained on images of cracks in civil works; the second stage performs the measurement of the detected crack (main length and predominant orientation), using techniques such as histogram of oriented gradients (HOG), skeletonization, and distance transform, as well as the conversion of pixel-based measurements into metric units. The results obtained in the detection stage (precision of 81.1%, recall of 69.7%, and mAP@50 of 71.1%) and in the measurement stage (average error of 2.24%), together with experiments using images from real building and civil construction environments and the simulation of a continuous monitoring scenario during drone flight, demonstrate the feasibility of the proposed method. Although opportunities for improvement were identified, such as expanding the training dataset and refining the calibration of the geometric measurement procedure, the present work advances the development of an integrated computational method that combines modern AI techniques, computer vision, and the use of drones to support inspections of buildings and civil engineering structures.