## 2 Research Methodology and Dataset
### Data Acquisition and Ground Truth for Validation  
In this study, five existing reinforced concrete bridge deck slabs were inspected and evaluated for sub-surface anomalies such as delamination. The bridges were between 47 and 49 years old and, at the time of investigation, were supporting the I-29 traffic. The spans range from 64 m to 142 m for Forest and Park River bridges, respectively [13; 18; 24; 25]. At an average height of 17 m above ground level (AGL), a DJI Matric 210 UAS equipped with a FLIR XT V2 infrared thermal camera was used for non-invasive IRT data collection (Table 1). A summary of data collection conditions, such as the data collection date, UAS specification, sensor specifications, ambient weather conditions, data quality, data preprocessing, and annotation, and the number of pixels, are discussed in detail in [PERSON] et al. [25]. Table 1 provides an overview of the FLIR XT V2 infrared thermal camera specifications for data collection, while Table 2 highlights an overview of the data collected from the bridge decks. The resolution of the stitched images is shown in Table 1. The set of image frames collected for each of the five investigated bridge decks was stitched to generate a single mosaic thermal image using Agisoft Metaphase 2021 @ software. The annotated IRT dataset used in this study and its detailed description are discussed in SDNET2021 [25; 26] and available via DOI at [[https://doi.org/10.31356/data019](https://doi.org/10.31356/data019)]([https://doi.org/10.31356/data019](https://doi.org/10.31356/data019)), accessed on 21 September 2023. The methodology developed in this study is shown in Figure 2. The raw images from SDNET2021 were annotated based on the number of proposed classes. Initially, the raw data was annotated as a two-class problem (classes 0 and 1). The sound and background were annotated as class 0, while the delaminated pixels were annotated as class 1. Figure 1: UNET encoder–decoder architecture. (Adapted from [PERSON] et al. [44].) This developed dataset was called the raw annotated dataset (RD). To evaluate any likely effect of annotation on the dataset and examine effective background segmentation, we further annotated the dataset differently as a background annotated dataset (BD) and a manually cleaned dataset (MD). The MD was also annotated as two-class segmentation problems (classes 0 and 1), where class 0 signified sound and class 1 signified delaminated pixels. The BD was a three-class problem (classes 0, 1, and 2) for the sound, delaminated, and background pixels. The number of delaminated, sound, and background pixels for each bridge is shown in Table 2. The three datasets adopted in this study are summarized as follows. 1. RD--The developed ground truth images were preprocessed such that the sound and background pixels were labeled zeros (0), and the delaminated pixels were labeled ones (1), showing a binary pixel annotation and classification problem. 2. MD--After sub-dividing the image of 32 \(\times\) 32 pixels, the blocks for the background pixels outside the bridge deck's ROI were manually hand-picked and excluded from the ground truth and corresponding dataset. This thereby reduces the number of \begin{table}
\begin{tabular}{l l} \hline
**Characteristics** & **Specifications** \\ \hline Thermal Resolution & 640 \(\times\) 512 pixels \\ \hline Full Frame Rates & 30 Hz (NTSC) 25 Hz (PAL) \\ \hline Spectral Band & 7.5–13.5 \(\upmu\)m \\ \hline Pixel Pitch & 17 \(\upmu\)m \\ \hline Thermal Imager/Detector Type & Uncooled VOx Microbolometer \\ \hline Digital Zoom & 2\(\times\), 4\(\times\) \\ \hline Field of View & 24\({}^{\circ}\times 19^{\circ}\) \\ \hline Focal Length & 9 mm \\ \hline \end{tabular}
\end{table} 
Table 1: Camera specifications for FLIR XT V2 thermal camera. Figure 2: Methodology and workflow. image blocks for each bridge deck. Similarly, the pixels were annotated as two classes (binary classification): sound (0) and delaminated (1).