Fly facial recognition based on deep convolutional neural network

被引：0

作者：

Chen Y.-T. ^{[1
]}

Chen W.-N. ^{[1
]}

Zhang X.-Z. ^{[1
]}

Li Y.-Y. ^{[1
]}

Wang J.-S. ^{[1
]}

机构：

[1] College of information science and technology, Dalian Maritime University, Dalian

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2020年 / 28卷 / 07期

关键词：

Deep convolutional neural network; Face recognition of fly; Inception-ResNet; Multi-task convolutional neural network; Reduction network;

D O I：

10.37188/OPE.20202807.1558

中图分类号：

学科分类号：

摘要：

Given the large number of species of flies and their individual complex characteristics, recognizing a particular type of fly has proved to be time consuming and, for the most part, inaccurate. In this paper, a method for the facial recognition of a fly using deep learning technologies was proposed, specifically a Convolutional Neural Network (CNN), and its related face recognition algorithms. Initially, a multi-task convolutional neural network was utilized and optimized for the image alignment process. Thus, depth-wise separable convolutions were applied to reduce the number of calculation parameters as well as the image preprocessing time. Next, we combined the rough extraction of contour features and fine extraction of specific parts to derive more abundant feature information. The convolution and pooling layers were harnessed to elicit contour eigenvalues of the image, while Inception-ResNet and Reduction networks were administered simultaneously to obtain eigenvalues of specific parts. Finally, the above methods were coalesced to enhance the accuracy and comprehensibility of the resultant feature information for network training. Experimental results show that the mean average precision of the proposed method is 94.03%. When compared with other network training methods, this method not only improves the computational efficiency but also ensures high accuracy. © 2020, Science Press. All right reserved.

引用

页码：1558 / 1567

页数：9

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