Study on the Combined Application of CFAR and Deep Learning in Ship Detection |
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| Authors: | Ruifu Wang Jie Li Yaping Duan Hongjun Cao Yingjie Zhao |
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| Institution: | 1.College of Geomatics,Shandong University of Science and Technology,Qingdao,China;2.The Key Lab of Surveying and Mapping Technology on Island and Reef,SBSM,Qingdao,China;3.Geomatics Center of Zhejiang,Hangzhou,China |
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| Abstract: | To maintain national socio-economic development and maritime rights and interests, it is necessary to obtain the space location information of various ships. Therefore, it is important to detect the locations of ships accurately and rapidly. At present, ship detection is mainly carried out by combining satellite remote sensing imaging with constant false alarm rate (CFAR) detection. However, with the rapid development of satellite remote sensing technology, remote sensing data have gradually begun to show the characteristics of “big data”; additionally, the accuracy and speed of ship detection can be improved by analysing big data, such as by deep learning. Thus, a ship detection algorithm that combines CFAR and CNN is proposed based on the CFAR global detection algorithm and image recognition with the CNN model. Compared with the multi-level CFAR algorithm that is based on multithreading, the algorithm in this paper is more suitable for application to ship detection systems. |
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