## 6 Conclusions  
This article proposes a methodology to automatically detect corrosion in the roofing systems of large-scale industrial buildings. First, the procedure relies on setting up an image database composed of more than 8k high-resolution images with the support of a UAV vision system. Second, the procedure entails the application of advanced image processing techniques based on the Mask R-CNN deep learning framework. The UAV used was the DJI MAVIC Enterprise Advanced, equipped with an RTK system that can provide the estimated position in real-time and, therefore, the ability to register high-accuracy georeferenced images. Finally, the images dataset, containing about 18k instances of corrosion, was annotated with the VIA software and processed in a JSON file compatible with the AI framework.  
The training of the Mask R-CNN model involved tuning some hyperparameters from the advanced library made available by the Facebook AI research team, known as Detectron2. The adjusted hyperparameters were the size of the input images, the data augmentation strategy, the value of the RoI IoU Head hyperparameter, and the backbone network. The results are consistent for the training, validation, and test datasets. In terms of metrics, it is highlighted the average precision for detection and segmentation, considering an IoU of 50%, achieved values of 65.1% and 59.2%, respectively. Furthermore, the precision and recall computed reached 85.8% and 84.0% for all instances identified in the labeling process. Visually, the inferences show that the model can be trusted, identifying the anomalies even in the most complex backgrounds and lighting conditions. Indeed, the results of this research suggest a reliable and effective method for detecting corrosion on sandwich metallic panels, allowing for a long-distance, non-contact, low-cost, and automated inspection, culminating in cost savings within the facility management strategies of large-scale industrial buildings.  
As future improvements, the authors are developing an application to integrate a new type of anomaly in the instance segmentation model, such as mechanical damages and water puddle accumulation. Furthermore, a semi-supervised technique to be applied in the already-made database is also being studied, which will support the automatic annotation of the corrosion instances in similar contexts. Finally, the integration of the georeferenced anomalies derived from the AI model within 3D photogrammetric reconstructions of the roofing systems are also planned, as well as the real-time inference of the images with embedded UAV hardware (e.g., NVIDIA JETSON Orin).  
Conceptualization, R.L., R.C., D.R., R.S., V.A. and A.D.; methodology, R.L., R.C., D.R. and R.S.; software, R.L., R.C. and R.S.; validation, R.L., R.C. and R.S.; investigation, R.L., R.C., D.R. and R.S.; resources, R.L., R.C., D.R. and R.S.; writing--original draft preparation, R.L.; writing--review and editing, R.C., D.R., V.A. and A.D.; visualization, R.L. and R.C.; supervision, D.R., R.S., V.A. and A.D.; project administration, D.R.; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by: Base Funding--UIDB/04708/2020 and Programmatic Funding--UIDP/04708/2020 of the CONSTRUCT--Instituto de l&D em Estruturas e Construcoes funded by national funds through the FCT/MCTES (PIDDAC), as well as the R&D project INSPECDRONE--Identification of anomalies in the external envelope of industrial buildings based on AI techniques and supported by UAVs, financed by the company Multiprojectus/Garcia Garcia. Additionally, the author [PERSON] acknowledges the Universidade Federal de Ouro Preto (UFOP) and the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) and the second author, [PERSON], the doctoral grant UI/BD/150970/2021--Portuguese Science Foundation, FCT/MCTES.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy restrictions.  
The authors declare no conflict of interest.