Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

Santos, Ricardo; Ribeiro, Diogo; Lopes, Patrícia; Cabral, Rafael; Calçada, Rui

doi:10.1016/j.autcon.2022.104324

Cited by 16 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

mentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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).

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%