In this paper, we developed techniques to identify and quantify the damage (crack) to bridges based on images obtained by the unmanned aerial vehicle (UAV). The scope of the research includes image acquisition using UAV, the classification system of crack based on Deep-learning and algorithms of detection and quantification using improved Image Processing Techniques (IPTs). A conventional crack detection method using only IPTs can be applied marginally according to the image acquisition environment (lights, shadows, etc.), so we proposed the techniques based on Deep-learning to find the crack part in the region of interest (ROI) from the other types of damage or non-crack. After classifying the crack part in the ROI, improved IPTs are applied to the detected regions to quantify cracks at 300 micrometers. Performances of the technique were evaluated through preliminary test and field test. The non-contact bridge damage detection technology using UAV can be applied to the actual bridge inspection field It is expected to have more performance than existing bridge inspection methods.
Jin-Hwan Lee, Sung-Sik Yoon, In-Ho Kim, and Hyung-Jo Jung, "Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV)," Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 1059811 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 06, 2018; Published: 27 March 2018); https://doi.org/10.1117/12.2296691.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 12,000 conference presentations, including many plenary and keynote presentations.