Ship Detection Framework Based on Deep Learning Network
| Autor: | Lei Liu, Jing Ye, Yu-fen Sun, Gang Liu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning 05 social sciences Feature extraction 010501 environmental sciences computer.software_genre 01 natural sciences Task (project management) Image (mathematics) Feature (computer vision) 0502 economics and business Stage (hydrology) Data mining Artificial intelligence 050207 economics business computer 0105 earth and related environmental sciences Network model |
| Zdroj: | DEStech Transactions on Computer Science and Engineering. |
| ISSN: | 2475-8841 |
| DOI: | 10.12783/dtcse/iteee2019/28739 |
| Popis: | Ship detection and tracking have been recognized as a challenging task in the maritime administration. This paper focuses on the maritime traffic situation, inspects and produces the ship dataset. We improved the existing deep learning method through experiments. It is mainly reflected in the addition of feature reused dense blocks which are used in the feature extraction stage and the addition of contextual information in the low-scale feature map which is used in the multi-scale prediction stage. The improved network model can effectively identify and calibrate the ship image in the dataset, thus improving maritime surveillance efficiency. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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