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Object Augmentation for Automated Multi-damages Construction Detection using Lightweight Deep Learning경량 딥러닝을 이용한 다중 손상 자동탐지를 위한 객체 증강
- Other Titles
- 경량 딥러닝을 이용한 다중 손상 자동탐지를 위한 객체 증강
- Issue Date
- Apr-2022
- Publisher
- 한국구조물진단유지관리공학회
- Keywords
- 손상감지; 경량 딥러닝; Damages detection; Lightweight Deep Leaning
- Citation
- 한국구조물진단유지관리공학회 2022년도 봄 학술발표회 논문집, v.26, no.1, pp 69 - 69
- Pages
- 1
- Indexed
- OTHER
- Journal Title
- 한국구조물진단유지관리공학회 2022년도 봄 학술발표회 논문집
- Volume
- 26
- Number
- 1
- Start Page
- 69
- End Page
- 69
- Abstract
- Computer Vision (CV) -based construction damages has been widely developed and resulted in potential alternative over traditional visual inspection. Howerver, the current deep learning models require an enormous data size, and relatively computational-expensive. Therefore, collecting and create more data has been an crucial state in the process of excecution, especially in the context of multi-damages detection with surround different types of object images. Therefore, an object aumentation and lightweight neural network has been introduced with the aim to improve the computional perfomance and address limitation of shortage and imbalance data
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Related Researcher
- Ahn, Yong Han
- ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)