Automated Scanning of Concrete Structures for Crack Detection and Assessment Using a Drone

Smaoui, Amna; Yaddaden, Yacine; Chérif, Raef; Lamouchi, Dorra

doi:10.1109/sta56120.2022.10019224

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can swiftly assess damage in hard-to-reach areas, locate survivors, and identify potential hazards, which significantly enhances the effectiveness and safety of response efforts. Furthermore, in industries like energy, drones are used for inspecting infrastructure such as power lines, wind turbines, and oil rigs [6,7]. This reduces the need for risky manual inspections and helps ensure the reliability and safety of energy production.…”

Section: Introductionmentioning

confidence: 99%

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

Kozlov,

Kondratenko,

Skakodub

2024

JMM

View full text Add to dashboard Cite

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

Kozlov,

Kondratenko,

Skakodub

2024

JMM

View full text Add to dashboard Cite

show abstract

“…As a new method for obtaining crack 2 of 15 images, UAVs are widely used [8,9]. Smaoui et al [10] proposed a method for planning paths for autonomous scanning tasks of concrete structures based on onboard cameras, collecting and transmitting images to estimate the extent of damage caused by cracks. Ngo et al [11] used drones to collect images of cracks and defects on the surface of concrete bridges, developing an effective method for assessing the structural integrity of concrete bridges.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning- and Unmanned Aerial Vehicle-Based Structural Crack Detection in Concrete

Jin,

Zhang,

Chen

et al. 2023

Buildings

View full text Add to dashboard Cite

Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks.

show abstract