고신뢰도 학습데이터 생성을 통한 수중 소나이미지의 물체인식 기법
- Title
- 고신뢰도 학습데이터 생성을 통한 수중 소나이미지의 물체인식 기법
- Authors
- YU, SON-CHEOL; SUNG, MINSUNG; KIM, JUHWAN
- Date Issued
- 2022-05-13
- Publisher
- 한국로봇학회
- Abstract
- Underwater object detection in sonar images requires a large number of images of target objects. For easy and accurate object detection, this paper propose a method using convolutional neural network (CNN) training with a sonar image simulator. Instead of simulating very realistic sonar images which is computationally complex, the training images are simulated by calculating only simple semantic information and adding randomized degradation effects to the simulated images. Images with randomized noise make CNN robust to noise. As a result, CNN can detect the target at sea, without training dataset consisting of real underwater sonar images captured at a field which are difficult to obtain.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/112661
- ISSN
- 2234-7194
- Article Type
- Conference
- Citation
- 제17회 한국로봇종합학술대회(2022 KRoC), page. 551 - 552, 2022-05-13
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.