Learning Deep Representations and Detection of Docking Stations using Underwater Imaging
文献类型：会议论文
| 作者 | Okatani, Takayuki1,2; Liu S(刘爽)2,3,4; Xu HL(徐红丽)4 ; Lin Y(林扬)4; Gu HT(谷海涛)4; Ozay, Mete2
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| 出版日期 | 2018 |
| 会议日期 | May 28-31, 2018 |
| 会议地点 | Kobe, Japan |
| 关键词 | Underwater imaging underwater docking CNNs detection |
| 页码 | 1-5 |
| 英文摘要 | Underwater docking endows AUVs with the ability of recharging and data transfer. Detection of underwater docking stations is a crucial step required to perform a successful docking. We propose a method to detect underwater docking stations using two dimensional images captured under different environmental light variance, deformations aroused by scale and rotation, different light intensity and partial observation. In order to realize our proposed method, we first train Convolutional Neural Networks (CNNs) to learn feature representations and then employ a deep detection network. In order to analyze the performance of the proposed method, we prepared an image dataset of docking stations using underwater imaging. Then, we explore the performance of our method using different data augmentation methods. We improved the AUC of detection by 0.14 using data augmentation and obtained 0.88 AUC with data augmentation. An increment of 0.23 AUC is gained by transfer learning and we obtained 0.88 AUC on another datasets. |
| 产权排序 | 1 |
| 会议录 | OCEANS 2018 MTS/IEEE Kobe
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-5386-1654-3 |
| WOS记录号 | WOS:000465206800091 |
| 源URL | [http://ir.sia.cn/handle/173321/23784]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 通讯作者 | Liu S(刘爽) |
| 作者单位 | 1.RIKEN Center for AIP, Tokyo, Japan 2.Graduate School of Information Sciences, Tohoku University, Sendai, Japan 3.University of Chinese Academy of Sciences, Beijing China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Okatani, Takayuki,Liu S,Xu HL,et al. Learning Deep Representations and Detection of Docking Stations using Underwater Imaging[C]. 见:. Kobe, Japan. May 28-31, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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