Open AccessExtraction of Ship Images using Deep Learning
Author(s) -
G. K. Pavithra,
S Shridevi
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9682.069520
Subject(s) - hull , deep learning , computer science , artificial intelligence , artificial neural network , extraction (chemistry) , enhanced data rates for gsm evolution , convolutional neural network , productivity , marine engineering , pattern recognition (psychology) , computer vision , engineering , chemistry , chromatography , economics , macroeconomics
Ship Extraction is very important in the marine industry. Extraction of ships is helpful to the fishers to find the other ships nearly around the particular area. Still today the fishers are to find the ships using some traditional methods. But now it became difficult due to environmental changes. So, by using the deep learning techniques like the CNN algorithm the ship extraction can be identified effectively. Generally, the ships are identified as narrow bow and parallel hull edge, etc. Here, the Existing system they have used the Tensor flow, to see the performance of the datasets, using Recall and precision. In the proposed system, we are using CNN deep learning techniques to identify the ships. By finding the ships with the techniques, the time will be saved and the productivity can be increased. The features of the ship image are taken and trained using the neural network algorithm and then the prediction is done by testing the images.