Open AccessMenfish Classification Based on Inception_V3 Convolutional Neural Network
Author(s) -
Jiangchang Huang,
Wenping Gong,
Hong Chen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/677/5/052099
Subject(s) - convolutional neural network , computer science , fishing , artificial intelligence , identification (biology) , fish <actinopterygii> , artificial neural network , feature (linguistics) , machine learning , deep learning , pattern recognition (psychology) , fishery , ecology , linguistics , philosophy , biology
Due to the impact of global environmental pollution deterioration, as well as uncontrolled fishing activities. The global fish stocks are becoming more and more serious, so it is necessary to pay attention to this issue. In this paper, the characteristics of traditional fish identification methods are vast, subjective factors are strong, and the current accuracy is not good in today’s deep learning. A migration learning based on Inception_v3 network with excellent feature extraction ability is proposed. Fish recognition model This model has better recognition ability than traditional models and some simple convolutional neural networks, and further improves the accuracy of Inception_v3 for fish recognition.