Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals
| Autor: | Zhihong Pan, Tian Tian, Xiangyu Tan, Zhengquan Chu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Pooling Feature extraction 0211 other engineering and technologies Context (language use) 02 engineering and technology computer.software_genre context feature pooling Minimum bounding box rotation region 0202 electrical engineering electronic engineering information engineering lcsh:Science 021101 geological & geomatics engineering Skew Pipeline (software) inshore ship detection Feature (computer vision) region proposal network General Earth and Planetary Sciences 020201 artificial intelligence & image processing lcsh:Q Data mining computer Rotation (mathematics) multi-scale feature fusion |
| Zdroj: | Remote Sensing; Volume 12; Issue 2; Pages: 339 Remote Sensing, Vol 12, Iss 2, p 339 (2020) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12020339 |
| Popis: | Inshore ship detection plays an important role in many civilian and military applications. The complex land environment and the diversity of target sizes and distributions make it still challenging for us to obtain accurate detection results. In order to achieve precise localization and suppress false alarms, in this paper, we propose a framework which integrates a multi-scale feature fusion network, rotation region proposal network and contextual pooling together. Specifically, in order to describe ships of various sizes, different convolutional layers are fused to obtain multi-scale features based on the baseline feature extraction network. Then, for the purpose of accurate target localization and arbitrary-oriented ship detection, a rotation region proposal network and skew non-maximum suppression are employed. Finally, on account of the disadvantages that the employment of a rotation bounding box usually causes more false alarms, we implement inclined context feature pooling on rotation region proposals. A dataset including port images collected from Google Earth and a public ship dataset HRSC2016 are employed in our experiments to test the proposed method. Experimental results of model analysis validate the contribution of each module mentioned above, and contrast results show that our proposed pipeline is able to achieve state-of-the-art performance of arbitrary-oriented inshore ship detection. |
| Databáze: | OpenAIRE |
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