碩士 / 國立臺北科技大學 / 電機工程系 / 107 / Synthetic aperture radar (SAR) is a radar with superior traversal. The radar emits energy, then get the reflects after reaches the surface. Compared with visible light, it can easily penetrate the clouds and is not affected by climate conditions. SAR has a wide range of object detection and monitoring, and produce high-resolution images, also has been widely used in aviation and spacecraft.
This study obtained the ship and oil spill dataset(SOSD) and SAR ship detection dataset(SSDD). We enhanced the training samples to improve the detection accuracy.
These two datasets enable further verification and comparison.
The deep learning method for ship target detection we use is YOLOv3 (You Only Look Once version 3). Compared with YOLOv2, multi-size feature fusion is used so the average detection is added. Although cost time is increased a little bit, but the target for small size has a better accuracy, which increased from 94% to 97%.
| Identifer | oai:union.ndltd.org:TW/107TIT00441064 |
| Date | January 2019 |
| Creators | CHEN, WEI-LUN, 陳威倫 |
| Contributors | CHANG, YANG-LANG, 張陽郎 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 53 |
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