## 1 Introduction
However, the proposed methodology proved limited since the counting was only based on the transversal section of the rebars. The high average precision achieved also draws attention, indicating that the model's generalization might be compromised when applied to distinct scenarios in complex backgrounds. Nevertheless, this innovative idea inspired other studies, such as the one developed by [PERSON] and [PERSON] [14], that proposed a complete methodology to perform steel rebar assessment, resorting to high-performance hardware. In this study, the Mask R-CNN algorithm, with the support of a stereo vision system, was upgraded to measure not only the number of rebars but also the spacing, length, and diameter of the rebars. However, the dataset, containing only 240 images, was the main drawback of the work. Similarly, [PERSON] and [PERSON] [15] developed a large-scale dataset for machinery operating on the construction site, including a reliable labeling method that enhances detection and classification. Despite this performance, further improvements can benefit the work since it only includes segmentation annotations for particular cases. Furthermore, all the images were taken from the ground level, which is less efficient and more timing consuming when compared to aerial acquisition. The indoor tracking of the construction process was addressed by [PERSON] et al. [16] with the Mask-RCNN algorithm and a stereo camera to capture 738 images and monitor the execution progress of a base floor including coatings, with the results being transferred to a BIM digital model. This study had the challenge of extrapolating the learning to other building construction stages. In the area of waste management and disposal, which is a current topic of concern, [PERSON] et al. [17] applied semantic segmentation to recognize the composition of construction waste (e.g., rock, stone, packaging, fabric, and wood, etc. ), based on a DeepLabv3+ network [18], and achieved a Mean Intersection Over Union (mIoU) of 56%. [PERSON] et al. [19] proposed the application of the Mask R-CNN to estimate the overall built area in rural regions, using open-source satellite images and a transfer learning strategy for the training stage, as well as UAV-acquired images for the test/inference stage. However, this study did not take full advantage of the UAV images, missing the opportunity to use detailed high-resolution images in the training stage. Damage assessment is currently a significant concern for infrastructure managers and is where most studies involving advanced image processing are performed. [PERSON] et al. [20] proposed a semi-supervised methodology to detect spalling in real-time, providing a 30% improvement in precision compared to a human inspector. Moreover, [PERSON] et al. [21] classified exposed steel rebar images from an industrial building using a CNN, innovatively using the support of a UAV to obtain orthomosaic maps with the identified anomalies. Instance segmentation was also performed for the damage assessment, but this technique is still underused when compared to other AI algorithms [10]. [PERSON] et al. [22] used the Mask R-CNN framework and aerial images to precisely identify damaged buildings after the Kumamoto earthquake in 2016, reaching 88% of accuracy but lacking the report of the segmentation metrics. In addition, [PERSON] et al. [23] applied the Mask R-CNN using ground penetrating radar images to automatically detect and segment abnormal instances that might indicate corrosion concrete bridges, reaching an average accuracy for detection and segmentation of 58.6% and 47.6%, respectively. Corrosion defects were also identified with machine learning in several applications involving bridges and buildings [24; 25; 26]. It is worth noticing other potential applications within the automatic detection of defects in welded joints [27]. However, none of these authors explored the potential of the Mask R-CNN to detect corrosion in metallic structures, with the exception of [PERSON] et al.