3D-Modeling Dataset Augmentation for Underwater AUV Real-time Manipulations*

Chua Chin Wang, Chia Yi Huang, Chu Han Lin, Chia Hung Yeh, Guan Xian Liu, Yu Cheng Chou

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Underwater real-time object recognition is essential to unmanned underwater drones, namely autonomous underwater vehicles (AUV), cruising in the ocean. As the deep learning technology evolves swiftly lately, the attempt for AUVs to fully understand the surrounding environment becomes an emerging demand for marine or military applications. No matter which approach that deep learning manages to adopt, a large dataset with sufficient number of images for each object is required. In this investigation, a dataset augmentation method based on 3D modeling is proposed to resolve the mentioned problem. By rotating and scaling the target object in 3 dimensions with different backgrounds, the number of underwater object images is increased over 1000 times. Through the proposed method, high quality image data are forged to improve the recognition accuracy of those rare underwater objects, which are very hard to collect enough number of images, by 20% based on real-time video clips' experiment.

原文英語
主出版物標題Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020
編輯Xuan-Tu Tran, Duy-Hieu Bui
發行者Institute of Electrical and Electronics Engineers Inc.
頁面145-148
頁數4
ISBN(電子)9781728193960
DOIs
出版狀態已發佈 - 2020 十二月 8
對外發佈
事件16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020 - Virtual, Halong, 越南
持續時間: 2020 十二月 82020 十二月 10

出版系列

名字Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020

會議

會議16th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2020
國家/地區越南
城市Virtual, Halong
期間2020/12/082020/12/10

ASJC Scopus subject areas

  • 能源工程與電力技術
  • 電氣與電子工程
  • 安全、風險、可靠性和品質
  • 人工智慧
  • 電腦網路與通信

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