Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88686
Title: Intelligent image recognition system for marine fouling using softmax transfer learning and deep convolutional neural networks
Authors: Chin, C. S.
Si, JianTing
Clare, A. S.
Ma, Maode
Keywords: Convolutional Neural Network (CNN)
DRNTU::Engineering::Electrical and electronic engineering
Image Recognition
Issue Date: 2017
Source: Chin, C. S., Si, J., Clare, A. S., & Ma, M. (2017). Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks. Complexity, 2017, 5730419-. doi:10.1155/2017/5730419
Series/Report no.: Complexity
Abstract: The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.
URI: https://hdl.handle.net/10356/88686
http://hdl.handle.net/10220/45854
ISSN: 1076-2787
DOI: 10.1155/2017/5730419
Rights: © 2017 C. S. Chin et al. 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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