# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 5 Conclusions and Future Work  
This paper presents a study highlighting the effectiveness of implementing pixel-level deep crack segmentation models on historical surfaces. The significance of the proposed approach is revealed through adopting variants of the U-Net deep learning model for handling segmentation problems with limited amounts of data.  
Thus, in this paper, the proposed automated approach examined three U-Net-based deep learning models, namely, Deep ResU-Net, ResU-Net++, and \(U^{2}\)-Net, for pixel-level crack segmentation. Decisively, it was observed that the \(U^{2}\)-Net semantic segmentation neural network model outperformed other tested U-Net models based on its ability to recognize more contextual information from multi-scales and increase the whole architecture's depth with a reasonable computation cost.  
Moreover, in order to tackle the challenge of limited historical surface data in the literature, a primary dataset is generated, for testing the proposed approach, containing crack images of historical surfaces with various crack types, sizes, and severity on several complex backgrounds of ornaments, carvings, wood patterns, separators, and corrosion. To the best of the authors' knowledge, this research is the first to utilize the U-Net deep learning model for pixel-level deep crack segmentation on images of historical surfaces.  
It is observed that the performance of the proposed approach using the \(U^{2}\)-Net model deteriorates, with Dice score, mIoU, and Jaccard measures declining from 80.52% to 71.09%, from 83.78% to 78.38%, and from 67.392% to 55.147%, respectively, when tested on the historical surfaces dataset compared to when tested on the open crack dataset [7]. The \(U^{2}\)-Net model is known for detecting salient objects, which results in some cases of mistakenly detecting the boundaries as cracks. However, despite this observation, it still surpasses the performance reported in the literature considering the challenges previously stated regarding crack detection on historical surfaces. Furthermore, several state-of-the-art variant U-Net models are examined for their efficacy to classify crack images from historical surfaces on a pixel level, with the highest obtained Dice score of 71.09%. In particular, for historical surfaces, when the ResU-Net++ is considered with contrast stretching, the Dice score increases from 37.98% to 61.42%, which demonstrates the beneficial effect of using contrast stretching as a preprocessing step during the testing phase.  
The most significant findings of the proposed approach in this research surpassed those of the other proposed approaches in the literature with comparable experiments for crack segmentation on concrete or asphalt surfaces. However, several challenges still remain to be addressed for future research, such as running more experiments considering other types of surfaces. Furthermore, although the proposed deep segmentation approach achieved promising results considering the crack defect on historical surfaces, generating more annotated images considering other defect types, such as corrosion, bird droppings, and shadows, should be considered for future research work in order to enrich the currently available dataset. Moreover, additional experiments considering images under low lighting and/or other environmental conditions should reveal the sensitivity of the proposed approach to image quality.  
E.E.: Data Collection, Initial Filtering, Data Preparation, Annotation, Methodology, Software, Validation, Writing--Original Draft. N.E.-B.: Data Collection, Data Preparation, Annotation, Supervision, Writing--Review and Editing. S.A.T.: Initial Filtering, Annotation, Supervision, Writing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.