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경량 딥러닝을 이용한 다중 손상 자동탐지를 위한 객체 증강
Object Augmentation for Automated Multi-damages Construction Detection using Lightweight Deep Learning
딘윈넉현 ( Dinh Nguyen-ngoc-han ) , 안용한 ( Ahn Yong-han )
UCI I410-ECN-0102-2023-500-001040658
이 자료는 4페이지 이하의 자료입니다.

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

[자료제공 : 네이버학술정보]
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