## 4 Conclusions  
This paper presents the findings of the study on sub-surface delamination detection in bridge decks. UNET deep convolutional neural network architecture was adopted. The network was trained with a first-of-its-kind publicly available bridge dataset SDNET2021. This study largely differs from previous studies where the investigation of delamination is carried out on mostly laboratory samples. In addition, the effect of different annotations and preprocessing approaches was assessed. The results were compared with iterative image-based methods conducted on the same dataset. The outcome of the investigation showed prospects in delamination detection. The summary of our findings is hereby highlighted herein:  
1. Preprocessing and annotation approach adopted before model development had a significant effect on the outcome of the results.
2. The model trained with the raw annotated dataset (RD) with base hyper-tune parameters without pixel balancing and augmentation showed the best performance for all the bridges. This is the case when the global accuracy (GA) was used for the evaluation of the model's performance. The GA ranged from the least 0.644 for Park River North Bound (PRNB) to the highest value of 0.773 for Park River South Bound (PRSB).
3. The model trained with the raw annotated dataset (RD) with base hyper-tune parameters and pixel balancing showed the best performance for all the bridges. This is considered when at least three-out-of-six performance metrics were highest. All the bridges had the highest metrics for precision, true negative rate (TNR), and F1 score.
4. A combination of multiple metrics is more adequate for model evaluation. In this study, a combination of precision, true negative rate (TNR), and F1 score with the semantic segmentation maps were used to evaluate the models' performance and select the optimal model.
5. The models trained with the balanced datasets showed distinctive segmentation of the background, sound, and delaminated pixels compared to other models trained with manually cleaned and background annotated datasets (MD and BD).
6. The scale, shear, and rotation augmentation parameters had no significant effect on the performance of the models. Considering the models being balanced alone or with augmentation, there was a performance improvement based on precision, TPR, and FI scores.  
Further studies are required to achieve improved performance metrics and segmentation maps. Several models will be adopted to compare the results and outputs of the model to determine the most optimal model for delamination detection. It is important to assess the effect of image size and resolution on the performance of the models. It is noteworthy that the effect of reduction in image quality should be evaluated and investigated in future studies. A higher super-resolution quality image may improve the model's performance. Future studies should adopt image-based methods and deep learning for improved resultsand output. In addition, since hyperspectral imagery combines spectral and spatial features over the visual and IR spectrum, this will undoubtedly yield better model performance and will be considered in future studies.  
S.D.: Conceptualization, Methodology, Acquired the Research Funding, and Supervised Preparation of First Draft. E.I.: Methodology, Software, Data Collection and Curation, Visualization, Writing--Original Draft, Data Quality, Annotation, and Validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are available at the link: [[https://commons.und.edu/data/19](https://commons.und.edu/data/19)]([https://commons.und.edu/data/19](https://commons.und.edu/data/19)). (Last accessed on 17 January 2023).  
The authors would like to acknowledge [PERSON] for assisting in editorial works.  
The authors declare no conflicts of interest.