基于轻量级cnn的图像识别与生态物联网框架的海洋鱼类管理

Lulu Jia, XiKun Xie, Junchao Yang, Fukun Li, Yueming Zhou, Xingrong Fan, Yu Shen, Zhiwei Guo
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引用次数: 0

摘要

随着新兴信息技术的发展,传统的海洋鱼类管理方法由于成本高、耗时长、管理不准确等问题,正在慢慢被新方法所取代。海洋鱼类管理技术的更新也对智慧城市的创建有很大的帮助。然而,目前研究的一些新方法特异性太强,不适用于其他海洋鱼类,识别精度普遍较低。因此,本文提出了一个生态物联网框架,其中实现了一个轻量级的深度神经网络模型作为海洋鱼类的图像识别模型,记录为Fish-CNN。本研究完成了Fish-CNN的多次训练和评估,最终的分类准确率可以固定在89.89%-99.83%之间。最后通过与Rem-CNN、线性回归和多层感知器的对比,验证了本文方法的稳定性和优越性。
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Lightweight CNN-Based Image Recognition with Ecological IoT Framework for Management of Marine Fishes
With the development of emerging information technology, the traditional management methods of marine fishes are slowly replaced by new methods due to high cost, time-consumption and inaccurate management. The update of marine fishes management technology is also a great help for the creation of smart cities. However, some new methods have been studied that are too specific, which are not applicable for the other marine fishes, and the accuracy of identification is generally low. Therefore, this paper proposes an ecological Internet of Things (IoT) framework, in which a lightweight Deep Neural Networks model is implemented as a image recognition model for marine fishes, which is recorded as Fish-CNN. In this study, multi-training and evaluation of Fish-CNN is accomplished, and the accuracy of the final classification can be fixed to 89.89%–99.83%. Moreover, the final evaluation compared with Rem-CNN, Linear Regression and Multilayer Perceptron also verify the stability and advantage of our method.
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