The deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia

Hatır, Ergün; Korkanç, Mustafa; Schachner, Andreas; İnce, İsmail

doi:10.1016/j.culher.2021.07.004

Cited by 27 publications

(15 citation statements)

References 15 publications

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“…For a subgraph Ω to be divided, the evolution curve C can divide the gray scale pattern I ( x , y ) corresponding to the primitive Ω into foreground Ω f and background Ω b , and C f and C b are used to represent the gray value of pixels in the foreground Ω f and background Ω b areas, respectively. The energy function of the CV model is shown in [ 24 – 26 ]

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Section: Data-enhanced Segmentation Technology Of Grassland Silk Road...mentioning

confidence: 99%

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture

Bu¹,

Jiang

2022

Computational Intelligence and Neuroscience

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Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.

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Section: Data-enhanced Segmentation Technology Of Grassland Silk Road...mentioning

confidence: 99%

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture

Bu¹,

Jiang

2022

Computational Intelligence and Neuroscience

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“…Russo et al [1] studied material deterioration detection by drone technology, and found that this technique is an efficient method to support restoration analysis because it is low cost, fast and easy to use in material deterioration mapping. It has also been confirmed by various studies that UAV technology is a good method to overcome terrain limitations and to search for hidden front areas [18][19][20].…”

Section: Introductionmentioning

confidence: 83%

“…In addition, various studies in the literature report that the effect of water on stone structures tends to increase the acidity of atmospheric pollutants over time, which further increases erosion and color change in stones [11,12]. Failure to take control and precautions in stone deterioration caused by the effects of water can turn small-scale (for example, flaking, fragmentation, cracking) deterioration into larger-scale deterioration over time, and this may lead to loss of cultural traces in historical areas [13]. For this reason, pre-detection and mapping of material deterioration in order to take the necessary measures is one of the most important stages of restoration works in the protection of heritage in historical areas.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the size of the building and historical sites makes it difficult to examine the types of material deterioration, and on-site visual analysis and up-to-date inspections by experts can also lead to human-induced errors. The development of methods based on various technologies in order to reduce the labor force and margin of error in the solution of this situation brings great convenience for the detection of material deterioration in historical areas [13]. In addition, the use of digital tools to support mapping activities allows for more detailed results on façade analysis [14] leading to simplification of manual and direct survey procedures [1].…”

Section: Introductionmentioning

confidence: 99%

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Detection and documentation of stone material deterioration in historical masonry buildings using UAV photogrammetry: A case study of Mersin Sarisih Inn

KARATAŞ

Alptekin

KARABACAK>

et al. 2022

Mersin Photogrammetry Journal

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The province of Mersin is an area where stone material is concentrated in terms of its geographical region, and in many historical areas in the region, stone structures are subject to deterioration due to various effects and protection interventions are often needed in the region. However, in the past, the lack of knowledge about the detection of material problems in the region has caused serious damage to historical textures and structures in many cases. The aim of the study carried out in this context is to document the material problems of the "Sarışih Han", which is a historical masonry building located in the Tarsus district of Mersin and which is of great importance to be preserved in terms of the continuity of the cultural heritage of the region, and thus to document the material problems for the sustainable management of the structures in the geographical region to contribute to its detection and repair. In this context, material problems in the structure were documented using the UAV photogrammetry method. The results of the study are important in terms of forming a guideline for the determination of material problems in the restoration phases of stone structures in Mersin. The findings obtained in the study show that the main damage observed on the stone surface of the building is eroded by the effect of water, and the problems caused by the lack of repairs in the worn areas have grown and plant formations have occurred in almost all of the facades, and the plant formations have caused large parts of fragmentation on some facades. It is seen that the joint loss on the stone surfaces and the deterioration of plant formation have reached advanced levels as a result of the exposure of the building to the effects of sun and water caused by the strong climate for centuries. Based on the determination results obtained in the study, it is hoped that correct applications will be made within the scope of the recommendations to be presented in the restoration of material problems in the restoration applications to be made in the geographical region.

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“…Computer vision methods, however, are an effective solution, eliminating both human error and difficulties in the field. Scholars such as Hatir have used the Mask R-CNN algorithm to detect and map the degradation observed in archaeological sites [28,29].…”

Section: Digital Preservation Of Intangible Cultural Heritagementioning

confidence: 99%

MasterSu: The Sustainable Development of Su Embroidery Based on Digital Technology

Zhang

et al. 2022

Sustainability

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Su embroidery, as an intangible cultural heritage of China, is a treasure accumulated by human civilization, but it has been gradually fading from people’s view in recent years. To handle the problems of slow creative output, high learning difficulty, and low production efficiency, and to promote the sustainable development of Su embroidery, this study builds an automatic generation system of Su embroidery called MasterSu, based on the CorelDraw platform. The system can automate the generation of embroidery sketches through area texture filling and color recognition, which allows users to participate in the design process. Finally, the performance and usefulness of the system are verified through user experiments, and it is confirmed that the system can facilitate novice users to understand the embroidery culture, learn the embroidery techniques, and create their embroidery works through the system.

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The deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia

Cited by 27 publications

References 15 publications

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture

Detection and documentation of stone material deterioration in historical masonry buildings using UAV photogrammetry: A case study of Mersin Sarisih Inn

MasterSu: The Sustainable Development of Su Embroidery Based on Digital Technology

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