Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

Chin C. S.; Si JianTing; Clare A. S.; Ma Maode

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Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

机译：基于Softmax转移学习和深度卷积神经网络的船舶污垢智能图像识别系统。

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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.

机译：控制海洋生物污垢具有挑战性且成本高昂。希望在船体性能受到重大影响之前及早发现，特别是如果“修饰”是一种选择。这里，描述了一种用于在开发的早期阶段检测海洋污垢的系统。在这项研究中，结垢图像可以通过移动网络无线传输进行分析。提出的系统利用转移学习和深度卷积神经网络（CNN）通过对检测到的污垢种类和表面污垢密度进行分类来对污垢图像执行图像识别。使用Google的Inception V3模型和最后一层的Softmax进行转移学习，该模型是在10个类别和1825张图像的结垢数据库上进行的。实验结果为结垢检测和识别提供了可接受的精度。

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《Complexity》 |2017年第1期|共页
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Chin C. S.; Si JianTing; Clare A. S.; Ma Maode;
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Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

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