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Title |
Construction of Faster R-CNN Deep Learning Model for Surface Damage Detection of Blade Systems
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Authors |
장지원(Jiwon Jang) ; 안효준(Hyojoon An) ; 이종한(Jong-Han Lee) ; 신수봉(Soobong Shin) |
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DOI |
https://doi.org/10.11112/jksmi.2019.23.7.80 |
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Keywords |
블레이드; 딥러닝; Faster R-CNN; 객체 인식; 표면 결함; 터빈엔진 Blade; Deep learning; Faster R-CNN; Object detection; Surface damage; Turbine engine |
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Abstract |
As computer performance improves, research using deep learning are being actively carried out in various fields. Recently, deep learning technology has been applying to the safety evaluation for structures. In particular, the internal blades of a turbine structure requires experienced experts and considerable time to detect surface damages because of the difficulty of separation of the blades from the structure and the dark environmental condition. This study proposes a Faster R-CNN deep learning model that can detect surface damages on the internal blades, which is one of the primary elements of the turbine structure. The deep learning model was trained using image data with dent and punch damages. The image data was also expanded using image filtering and image data generator techniques. As a result, the deep learning model showed 96.1% accuracy, 95.3% recall, and 96% precision. The value of the recall means that the proposed deep learning model could not detect the blade damages for 4.7%. The performance of the proposed damage detection system can be further improved by collecting and extending damage images in various environments, and finally it can be applicable for turbine engine maintenance.
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