Application and Typical Case of Unmanned Aerial Vehicles Combined with Building 3D Modeling

Li, Minhui; Yin, Hang; Cheng, Dongji; Xu, Xiaoying; He, Ya Fei

doi:10.1088/1742-6596/1746/1/012012

Cited by 2 publications

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“…The human skeleton is the most direct determinant of clothing modelling, and the reference point and datum of human body are closely related to clothing [16]. Human body has 206 bones, which are divided into skull, trunk bone, and limb bone [17,18].…”

Section: Methodsmentioning

confidence: 99%

“…The grey image size of each part of the front and side projected to is normalised, and the local characteristic image of the human body is the training data of the neural network [15]. The steps to obtain training data are as follows: The human skeleton is the most direct determinant of clothing modelling, and the reference point and datum of human body are closely related to clothing [16]. Human body has 206 bones, which are divided into skull, trunk bone, and limb bone [17,18].…”

Section: D Projection Of Human Body Modelmentioning

confidence: 99%

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Application of convolution neural networks‐based hierarchical perception technology in 3D clothing designs

Hong

2022

IET Networks

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The purpose of this research is to solve the problems in the modern life, such as the inconvenience caused by fast life pace, the need to save time, the unsatisfactory products for consumers, the low brand awareness in the clothing industry, serious product homogenisation and low life quality. The study of the application of hierarchical perception technology in 3D (three‐dimensional) clothing designs is carried out based on CNN (convolutional neural networks). The study includes the training of CNN, the selection of 3D clothing models and 2D (two‐dimensional) projection of 3D human body models. A simulation experiment is conducted by taking 49‐year‐old males, 22‐year‐old males and 50‐year‐old females as research objects. The findings show that the training network can effectively identify the typical lines of the human's hindneck, chest, shoulders, and waist, and the accuracy of the front and side recognition of the human body can be reached as high as 93% on average. The effective identification of human characteristics is achieved. The study has an important impact on the technical field and clothing industry and also has profound theoretical and practical significance.

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Section: Methodsmentioning

confidence: 99%

Section: D Projection Of Human Body Modelmentioning

confidence: 99%

Application of convolution neural networks‐based hierarchical perception technology in 3D clothing designs

Hong

2022

IET Networks

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Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Nagasawa¹,

Mas

Moya

et al. 2021

Sci Rep

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Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model.

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Application and Typical Case of Unmanned Aerial Vehicles Combined with Building 3D Modeling

Cited by 2 publications

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Application of convolution neural networks‐based hierarchical perception technology in 3D clothing designs

Application of convolution neural networks‐based hierarchical perception technology in 3D clothing designs

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

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