| Title |
Development of Deep Learning-Based Damage Detection Prototype for Concrete Bridge Condition Evaluation |
| Authors |
남우석(Nam, Woo-Suk) ; 정현준(Jung, Hyunjun) ; 박경한(Park, Kyung-Han) ; 김철민(Kim, Cheol-Min) ; 김규선(Kim, Gyu-Seon) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.1.0107 |
| Keywords |
딥러닝; 의미론적 분할 모델; Mask-RCNN; 손상탐지; 평가 프로토타입 프로그램 Deep learning; Semantic segmentation model; Mask-RCNN; Damage detection; Evaluation prototype program |
| Abstract |
Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of
human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members. |