碩士 / 國立政治大學 / 資訊科學系碩士在職專班 / 106 / The objective of this thesis is to develop methods to detect and recognize civilian boats and war ships in satellite images based on deep learning approaches when only limited amount of data are available.
The concept of transfer learning is employed to take advantage of existing models. Owing to the restricted availability of certain categorical data, this thesis also used data augmentation techniques to generate and add samples into the training sets to improve the overall accuracy of ship detection.
After extensive model selection and parameter fine-tuning, the average precision (AP) of war ships and civilian boats has reached 0.816 and 0.908 respectively, and the overall mAP is 0.862. The developed framework is ready to be incorporated in a semi-automatic system to assist military personnel in facilitating the efficiency of image detection and interpretation.
This thesis is expected to lay the groundwork for more precise military facility detection models, thus improving efficacy of future military facility image detection systems.
| Identifer | oai:union.ndltd.org:TW/106NCCU5394034 |
| Date | January 2018 |
| Creators | Wu, Shin-Shian, 吳信賢 |
| Contributors | Liao, Wen-Hung, 廖文宏 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 59 |
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