Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

Kung, Ren-Yi; Pan, Nai-Hsin; Wang, Charles C.N.; Lee, Pin‐Chan

doi:10.1155/2021/5598690

Cited by 18 publications

(11 citation statements)

References 64 publications

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“…Other examples given include Stadler et al [110] and Zhang et al [111] that used deep learning for object tracking and Peralta et al [112] that used deep learning for 3D reconstruction [102]. More recent examples include Onishi et al [113] which used a CNN approach to construct a tree identification and mapping system using UAV RGB images and Kung et al [114] which proposed a CNN model for imagebased automated detection of building defects (e.g., cracks).…”

Section: E Computer Visionmentioning

confidence: 99%

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

McEnroe

Wang

Liyanage

2022

IEEE Internet Things J.

173

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The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ Unmanned Aerial Vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This paper provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue operations, acting as aerial wireless BSs and drone light shows). As guidance for researchers and practitioners, the paper also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.

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Section: E Computer Visionmentioning

confidence: 99%

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

McEnroe

Wang

Liyanage

2022

IEEE Internet Things J.

173

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“…Dizaji and Harris (2019) launched a CNN model for detecting surface cracks in concrete columns. In Kung, et al (2021) and Munawar, et al (2022), unmanned aerial vehicles (UAVs) were used to capture the defects in mid to high-rise buildings and CNN frameworks were developed for cracks detection.…”

Section: Automated Visual Recognitionmentioning

confidence: 99%

“…Conventional approaches of detecting defects typically require facility managers or maintenance engineers conducting manual inspections of the building (Kong, et al, 2018). When the building is a mid to high-rise structure, collecting inspection data becomes cumbersome and can even pose safety hazards (Kung, et al, 2021). With the advancements in computer vision (CV), unreliable access to inspection data and the timeconsuming, labourious, erroneous manual methods of detecting defects have been improved (Lundkvist, et al, 2014;Şimşek, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…DL models automatically learn features by training data (Nanni , et al, 2017). Although the DL-based defects detection studies such as Chen and Jahanshahi (2017); Dizaji and Harris (2019); Kung, et al (2021); Munawar et al (2022) are emerging, the computational difficulties associated with developing and training DL models have not been fully addressed Wang, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A deep learning-based building defects detection tool for sustainability monitoring

Ekanayake¹

2022

Proceedings of 10th World Construction Symposium 2022

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To ensure sustainability of buildings, detection of building defects is crucial. Conventional practices of defects detection from building inspection data are mostly manual and error prone. With the advancements in computer vision, imaging technology and machine learning-based tools have been developed for real-time, accurate and efficient defects detection. Deep learning (DL), which is a branch of ML is more robust in automatically retrieving elements’ semantics to detect building defects. Although DL algorithms are robust in object detection, the computational complexities and configurations of these models are high. Therefore, this study presents a process of developing a computationally inexpensive and less complicated DL model using transfer learning and Google Colab virtual machine to improve automation in building defects detection. Cracks is one of the major building defects that constraint the safety and durability of buildings thus hindering building sustainability. Building cracks images were sourced from the Internet to train the model, which was built upon You Only Look Once (YOLO) DL algorithm. To test the DL model, inspection images of five (05) buildings collected by the Facilities Management department of a University in Sydney city were used. The DL model developed using this process offers a monitoring tool to ensure the sustainability of buildings with its’ ability of detecting cracks from building inspection data in real time accurately and efficiently. Although the current model is built to detect cracks, this process can be employed to automated detection of any building defect upon providing the training images of defects.

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“…Fortunately, machine learning and unmanned aerial vehicles (UAV) are playing a key role in the industry of the Internet of Things (IoT), in fact, the Internet of Everything (IoE). Hence, we can try and use these UAVs along with image processing related ML to enhance and improve the performance of the crack detection methods due to fact that the Machine learning can provide the best results for the data/pictures collected from UAV source [15,16,17,18,19,20].The objective of this research is to investigate and perform a depth analysis of the latest crack detection techniques using Unmanned Aerial Vehicles (UcAV) and Machine Learning algorithms (MLA) especially CNN-SVM algorithm and compare our results with other ML algorithms, which are related to our research project. Convolutional neural networks (CNNs) are used to detect crack in images to do away with the extraction of crack features.…”

mentioning

confidence: 99%

Investigation and Analysis of Crack Detection using UAV and CNN: A Case Study of Hospital Raja Permaisuri Bainun

Sheng¹,

Din²,

Waseem³

et al. 2023

IJSECS

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Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF)that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of3.0 percent increased from 82.94%to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).

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Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

Cited by 18 publications

References 64 publications

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

A deep learning-based building defects detection tool for sustainability monitoring

Investigation and Analysis of Crack Detection using UAV and CNN: A Case Study of Hospital Raja Permaisuri Bainun

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