Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

By Dongho Kang, Young-Jin Cha. Comput. Aided Civ. Infrastructure Eng., 2018

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SDDNet: Real-Time Crack Segmentation (2020, methodology): “Subsequently, this approach was combined with an autonomous drone system [12].”
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images (2018, background): “Kang and Cha [7] highlight several challenges that need to be addressed before applying our approach outside of a manufacturing setting.”
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete (2018, background): “Due to popularity of Unmanned Aerial Systems (UASs) for structural health 199 monitoring and bridge inspection [63] applications of DCNNs in UAS-assisted inspections has begun to 200 attract researchers for more robust non-contact damage detection [43, 64, 65].”
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “Along with the developments in wireless data transmission techniques, several studies have been conducted that utilized UASs technologies to broaden vision-based inspection in SHM [14,153,154], as well as vibration-based techniques [155].”