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Appl. Sci. 2018, 8(6), 936; https://doi.org/10.3390/app8060936

Probabilistic Ship Detection and Classification Using Deep Learning

School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
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Received: 9 April 2018 / Revised: 15 May 2018 / Accepted: 2 June 2018 / Published: 5 June 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

For an autonomous ship to navigate safely and avoid collisions with other ships, reliably detecting and classifying nearby ships under various maritime meteorological environments is essential. In this paper, a novel probabilistic ship detection and classification system based on deep learning is proposed. The proposed method aims to detect and classify nearby ships from a sequence of images. The method considers the confidence of a deep learning detector as a probability; the probabilities from the consecutive images are combined over time by Bayesian fusion. The proposed ship detection system involves three steps. In the first step, ships are detected in each image using Faster region-based convolutional neural network (Faster R-CNN). In the second step, the detected ships are gathered over time and the missed ships are recovered using the Intersection over Union of the bounding boxes between consecutive frames. In the third step, the probabilities from the Faster R-CNN are combined over time and the classes of the ships are determined by Bayesian fusion. To train and evaluate the proposed system, we collected thousands of ship images from Google image search and created our own ship dataset. The proposed method was tested with the collected videos and the mean average precision increased by 89.38 to 93.92% in experimental results. View Full-Text
Keywords: ship detection; ship classification; ship dataset; deep learning; Faster R-CNN; autonomous ship; Intersection over Union (IoU) tracking; Bayesian fusion ship detection; ship classification; ship dataset; deep learning; Faster R-CNN; autonomous ship; Intersection over Union (IoU) tracking; Bayesian fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kim, K.; Hong, S.; Choi, B.; Kim, E. Probabilistic Ship Detection and Classification Using Deep Learning. Appl. Sci. 2018, 8, 936.

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