Mr. Jin-Hwan Lee Profile

Jin-Hwan Lee

at KAIST

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Publications (3)

Proceedings Article | 22 March 2021 Presentation + Paper

Missing area detection and damage mapping method based on field-of-view estimation in UAV-based bridge inspection

Gi-Hun Gwon, Sungsik Yoon, Jin Hwan Lee, In-Ho Kim, Hyung-Jo Jung

Proceedings Volume 11591, 115911B (2021) https://doi.org/10.1117/12.2583375

KEYWORDS: Unmanned aerial vehicles, Bridges, Inspection, Cameras, Imaging systems, UAV imaging systems, Sensors, Image analysis, Global Positioning System, Optical inspection

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Recently, numerous studies have been conducted on the UAV-based inspection of infrastructure including bridges, mainly in the United States, Europe and Asia. According to preliminary research and pilot projects, UAV-based bridge inspection has advantages such as safety, cost-time efficiency and access to hazardous areas. Also, above all, it enables objective assessment of the condition of the structure from captured images. However, there are several limitations to the practical application of UAV-based bridge inspection. In particular, damage present in areas where images have not been captured can lead to inappropriate condition assessment. Therefore, it is necessary to check whether the UAV has captured all images for the inspection area. In addition, even if damage is identified in the captured image, it is necessary to map where the image is located on the bridge. In this study, a missing area detection and damage mapping methodology based on the estimated field-of-view using sensor data from UAV systems is proposed. The framework of the proposed methodology consists of a total of 4 phases. First, phase 1 is aimed at converting GPS data to the location of the camera during a UAV flight using IMU data. In phase 2, the coordinates of the center point of the image are determined from the gimbal IMU data and working distance. And phase 3 is the process of calculating the field-of-view through the camera's focal length and working distance. In the preceding phases, the coordinates of each image captured by the UAV are determined. Based on these, missing area detection and damage mapping are performed in phase 4. The proposed missing area detection and damage mapping methodology is experimentally validated for concrete shear wall with artificial damages and the actual target bridge. As a result of experimental validation, the proposed methodology detected areas for which image capture was missing and provided a result of mapping the identified damage within adequate accuracy.

Proceedings Article | 27 March 2018 Presentation + Paper

Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV)

Jin-Hwan Lee, Sung-Sik Yoon, In-Ho Kim, Hyung-Jo Jung

Proceedings Volume 10598, 1059811 (2018) https://doi.org/10.1117/12.2296691

KEYWORDS: Bridges, Image processing, Unmanned aerial vehicles, Inspection, Detection and tracking algorithms, Cameras, Image acquisition, RGB color model, Image classification, Image sensors

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Proceedings Article | 27 March 2018 Paper

Challenging issues and solutions of bridge inspection technology using unmanned aerial vehicles

Hyung-Jo Jung, Jin-Hwan Lee, In-Ho Kim

Proceedings Volume 10598, 1059802 (2018) https://doi.org/10.1117/12.2300957

KEYWORDS: Bridges, Inspection, Unmanned aerial vehicles, Algorithm development, Cameras, Infrared cameras, Imaging devices, Infrared imaging, Safety, Global Positioning System

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