应用物体检测算法有效识别文物建筑的损坏情况

IF 2.1 3区 地球科学 0 ARCHAEOLOGY Archaeological Prospection Pub Date : 2024-06-27 DOI:10.1002/arp.1947
Huadu Tang, Yalin Feng, Ding Wang, Ruiguang Zhu, Liwei Wang, Shengwang Hao, Shan Xu
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引用次数: 0

摘要

文物建筑对任何地区的文化和政治都至关重要。适当的维护和监测对保护这些建筑至关重要。然而,人工检测既耗时又昂贵。我们提出了一种基于深度学习的检测框架,用于识别古建筑墙体的损坏情况。本研究采用的算法是 YOLOv5。通过比较其五个不同版本,我们决定使用 YOLOv5m 作为最准确的检测算法,其 mAP 为 0.801。确定的损坏类型为物理风化和游客划痕。实验选择了高分辨率图像,并对图像进行了有效识别。此外,应用的算法可以实时检测和识别季节性破坏源,这在本研究的视频测试中得到了证明。研究结果有助于开发一种用于健康监测的智能工具,其目标是在文物建筑的日常维护中实现快速和远程损坏检测。
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Application of Object Detection Algorithm for Efficient Damages Identification of the Conservation of Heritage Buildings

Heritage buildings are crucial for any area's cultural and political aspects. Proper maintenance and monitoring are essential for the conservation of these buildings. However, manual inspections are time-consuming and expensive. We propose a deep learning–based detection framework to identify the damages on the ancient architectural wall. The algorithm applied in this study is YOLOv5. Comparing its five different versions, it was decided to use YOLOv5m as the most accurate detection algorithm with a mAP of 0.801. The damage types identified are physical weathering and visitors' scratches. High-resolution images were selected for the experiment and effectively identified image. In addition, the applied algorithm allows real-time detection and the identification of seasonal sources of disruption, which is proved by the video test in this study. The findings contribute to the development of an intelligent tool for health monitoring with the goal of fast and remote damage detection in the routine maintenance of heritage buildings.

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来源期刊
Archaeological Prospection
Archaeological Prospection 地学-地球科学综合
CiteScore
3.90
自引率
11.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The scope of the Journal will be international, covering urban, rural and marine environments and the full range of underlying geology. The Journal will contain articles relating to the use of a wide range of propecting techniques, including remote sensing (airborne and satellite), geophysical (e.g. resistivity, magnetometry) and geochemical (e.g. organic markers, soil phosphate). Reports and field evaluations of new techniques will be welcomed. Contributions will be encouraged on the application of relevant software, including G.I.S. analysis, to the data derived from prospection techniques and cartographic analysis of early maps. Reports on integrated site evaluations and follow-up site investigations will be particularly encouraged. The Journal will welcome contributions, in the form of short (field) reports, on the application of prospection techniques in support of comprehensive land-use studies. The Journal will, as appropriate, contain book reviews, conference and meeting reviews, and software evaluation. All papers will be subjected to peer review.
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