Using 3D Scanning Techniques from Robotic Applications in the Constructions Domain

Mariniuc, Alexandru Marin; Cojocaru, Dorian; Manta, Liviu Florin; Dragomir, Andrei; Abagiu, Marian

doi:10.1109/icstcc55426.2022.9931768

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Based on the hands-on experience gained through previous research projects, the team acquired the practical ability to correctly scan and acquire point cloud datasets with various sensors [1]. In the following section, we present the reasoning behind choosing one type of sensory system over the other available systems.…”

Section: Data Capturingmentioning

confidence: 99%

“…In a previous paper, the research team analyzed the possibility of using some of the experience that it gained from the development and practical implementation of certain applications as a starting point in the development of a machine learning-based 3D scanning application in the construction domain. It is worth mentioning that LiDAR and ZED camera systems were used in acquiring real images of both interior and exterior parts of certain buildings [1].…”

Section: Introductionmentioning

confidence: 99%

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Building Surface Defect Detection Using Machine Learning and 3D Scanning Techniques in the Construction Domain

Mariniuc,

Cojocaru,

Abagiu

2024

Buildings

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The rapid growth of the real estate market has led to the appearance of more and more residential areas and large apartment buildings that need to be managed and maintained by a single real estate developer or company. This scientific article details the development of a novel method for inspecting buildings in a semi-automated manner, thereby reducing the time needed to assess the requirements for the maintenance of a building. This paper focuses on the development of an application which has the purpose of detecting imperfections in a range of building sections using a combination of machine learning techniques and 3D scanning methodologies. This research focuses on the design and development of a machine learning-based application that utilizes the Python programming language and the PyTorch library; it builds on the team′s previous study, in which they investigated the possibility of applying their expertise in creating construction-related applications for real-life situations. Using the Zed camera system, real-life pictures of various building components were used, along with stock images when needed, to train an artificial intelligence model that could identify surface damage or defects such as cracks and differentiate between naturally occurring elements such as shadows or stains. One of the goals is to develop an application that can identify defects in real time while using readily available tools in order to ensure a practical and affordable solution. The findings of this study have the potential to greatly enhance the availability of defect detection procedures in the construction sector, which will result in better building maintenance and structural integrity.

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Section: Data Capturingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%