سال انتشار:

2021

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عنوان انگلیسی مقاله:

An object detection approach for detecting damages in heritage sites using 3-D point clouds and 2-D visual data

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ترجمه فارسی عنوان مقاله:

یک رویکرد تشخیص شی برای تشخیص خسارت در میراث با استفاده از ابرهای نقطه سه بعدی و داده های بصری دو بعدی

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منبع:

Sciencedirect - Elsevier - Journal of Cultural Heritage , 48 (2021) 74-82: doi:10:1016/j:culher:2021:01:002

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نویسنده:

Rachna Pathak

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چکیده انگلیسی:

We propose a novel pipeline for structural damage detection on surfaces of complex heritage structures using visual 2D and 3D data. We use deep learning and computer vision to detect damages in images of heritage sites, and subsequently localize the detected damage on corresponding 3D models. This enables intuitive visualization, giving a concrete idea about the extent of damage in 3D space. To train deep learning models for damage detection, we curate a labeled database consisting of images of Ayutthaya – Wat Phra Si Sanphet Temple (situated in Thailand), essentially converting the damage detection problem into an object detection task. We consider the two most common kinds of damages occurring in heritage structures, namely Crack and Spalling. Models trained using these database are experimentally observed to be robust as they can detect damages among intricate architectural designs and backgrounds. Post- training, we test the model’s domain transferability by detecting damages on unseen rendered images from 3D Models of UNESCO World Heritage Site – Hampi (situated in India). We also present a comparison of the performance of different configurations of Faster-RCNN as the damage detection model over heritage structure data and demonstrate the obtained results.© 2021 Elsevier Masson SAS. All rights reserved.
Keywords: Deep learning | Object detection | Computer vision | 3D point clouds | Image rendering | Structural health monitoring

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قیمت: رایگان

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توضیحات اضافی:

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