## 1 Introduction  
The best strategies for the visual inspection of large-scale industrial buildings are still a challenge to be addressed by civil infrastructure engineers. Typically, it is a time-consuming activity, with high human risks and financial costs, that increases in complexity with the surveyed area. In recent years, Unmanned Aerial Vehicles (UAVs) have been incorporated into this task, enabling a remote and enhanced procedure. Metallic sandwich panels are a versatile solution with properties that ensure a simple on-site installation and durability. In these elements, corrosion represents an early damaged state that can be directly or indirectly responsible for critical failure mechanisms (e.g.,delamination, debonding, and perforation, etc. ), as well as serviceability constraints of the interior spaces of the assets related to loss of impermeability and water leakage. In recent years, essential developments in software and hardware have led to undeniable advances in Artificial Intelligence (AI) techniques, allowing several novel applications of the image pattern recognition [1], which decisively contribute to the solution of problems such the one of the automatic detection of corrosion on metallic sandwich panels. Furthermore, the combination of these advances with a technology that allows the remote survey of large areas and buildings in real-time [2], i.e., UAVs, could help to diminish the occurrence of fall from height accidents, which was the most critical risk factor associated with construction activities in Great Britain in 2022 [3] and also represents an important concern in the rest of the world [4]. Computer vision has made progress with incorporating deep learning techniques for pattern recognition, in this case, anomaly identification. The landmark of this success was the remarkable performance of the Convolutional Neural Network (CNN) architecture developed in 2012 by [PERSON] et al. [5], widely known as AlexNet, which won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [6]. Nowadays, the most advanced deep learning techniques for image analysis allow performing one or several of the following image recognition tasks, depending on the framework used and the level of information required [7; 8; 9]: (i) classification, for identification of the existence (or not) of anomalies, based on the classical CNN algorithm; (ii) detection, which additionally localizes the anomaly and specifies the type of anomaly, based on the so-called Region-CNN (R-CNN) algorithm, and (iii) segmentation, which additionally specifies which pixels belong to each of the identified anomalies, based on the Mask R-CNN algorithm. The Mask R-CNN allows instance segmentation, which consists of the consecutive application of the classification, detection, and segmentation. In the construction sector, the application of these algorithms can be performed in three distinct areas [10]: (i) health and safety, (ii) management and tracking, and (iii) damage assessment. In the first area, the development of technology to automatically detect the absence of the use of personal protective equipment is a concern. [PERSON] et al. [11] developed a methodology for detecting the use of safety helmets on construction sites. Based on transfer learning and the DenseNet network, these authors created a bounding-box regressor capable of surpassing common challenges of complex backgrounds, like scale variance and perspective distortion. The authors were the first to apply a deep learning technique to this problem successfully. The study points out that the proposed solution is competitive with other existing detection methodologies, like the YOLO's families. Applications involving asset management and construction progress are more common and broad. For example, [PERSON] et al. [12] created a methodology for rebar counting in on-site construction, based on an improved version of the YOLOV3 network [13]. They obtained an average precision for detection equal to 99.7% for an IoU of 50%.