Please use this identifier to cite or link to this item:
|Title:||Intelligent image recognition system for marine fouling using softmax transfer learning and deep convolutional neural networks||Authors:||Chin, C. S.
Clare, A. S.
|Keywords:||Convolutional Neural Network (CNN)
DRNTU::Engineering::Electrical and electronic engineering
|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
|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|
Files in This Item:
|Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks.pdf||4.2 MB||Adobe PDF|
Updated on Mar 21, 2023
Web of ScienceTM
Updated on Mar 16, 2023
Updated on Mar 22, 2023
Updated on Mar 22, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.