# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 1 Introduction  
Historic buildings constitute invaluable cultural assets, embodying significant historical and artistic heritage. However, these structures are particularly susceptible to deterioration resulting from environmental influences, climate change, and human activity. Among various forms of structural damage, crack formation is especially prevalent, posing substantial threats to structural integrity and diminishing both cultural and historical value [1]. Accordingly, accurate detection and timely remediation of cracks are essential components of heritage conservation efforts [2]. Heritage conservation encompasses a range of approaches, including preservation, rehabilitation, restoration, and reconstruction, all of which prioritize the retention of original materials and respect for historical context. The effectiveness of conservation efforts is further enhanced by the implementation of preventive strategies, routine maintenance, and active community participation [3]. Conventional crack detection methods predominantly rely on expert visual inspections, often supported by manual measurements to determine crack dimensions and distribution. Although these methods are straightforward, they are inefficient for large-scale surveys and prone to subjectivity and human error. The measurement discrepancies among different inspectors have been reported to exceed 8% [4]. In contrast, the deviation between crack measurements obtained using deep learning-based methods and those acquired manually with measuring tools ranges from 0.4% to 5% [5]. [PERSON] et al. [6] employed a fractal theory-based approach to analyze heritage surfaces. This method effectively distinguishes surface features, such as impressions, cracks, and wear, at specific observational scales. To improve accuracy and efficiency, image processing techniques such as edge detection and thresholding have been employed. However, these methods often underperform in conditions with uneven lighting or complex backgrounds, necessitating manual adjustment for image preprocessing and parameter tuning [7; 8]. Recent advancements in deep learning, particularly in Convolutional Neural Networks (CNNs), have significantly improved crack detection performance [9; 10]. CNN-based models, widely adopted in computer vision, can be broadly categorized into image classification, object detection, semantic segmentation, and instance segmentation models. Image classification models such as AlexNet [11], GoogleNet [12], and ResNet [13] are designed to categorize an entire image. In contrast, object detection models, including Faster R-CNN [14], YOLO [15], and SSD [16], identify both the location and class of multiple targets within an image. Semantic segmentation models, such as FCN [17], U-Net [18], and SegNet [19], assign a category to each pixel, providing fine-grained analysis. Instance segmentation models, including Mask R-CNN [20] and later versions of YOLO [21], combine object detection and semantic segmentation to distinguish individual objects along with their pixel-level boundaries. While functionally related, classification models assign a single label per image, whereas detection and segmentation models identify multiple regions or features. Numerous studies have explored the application of deep learning models in crack detection. Object detection models are frequently used for rapid localization of cracks [22; 23; 24; 25; 26], while semantic segmentation models enable detailed pixel-level analysis, facilitating the measurement of crack morphology [27; 28; 29; 30; 31]. However, relatively few studies employ a combination of different model types. One contributing factor is the emergence of instance segmentation models, which already integrate detection and segmentation capabilities and have been applied to crack measurement [32; 33; 34]. Nonetheless, such models typically require high-performance computing resources for training and optimal inference.