基于深度学习技术的鱼类分类识别

P. Varalakshmi, J. Julanta Leela Rachel
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引用次数: 7

摘要

目前渔业中最重要的方面是鱼类的分类和定位。由于分割问题、噪声和环境条件的变化,很难对图像进行很好的分类。然而,对更高精度的目标识别有很大的需求。因此,为了解决这一问题,使用了卷积神经网络,它有助于以更好的精度对图像进行分类和定位。通过训练数据集对鱼类进行分类,并通过使用不同的激活函数来提高准确率。经过多次比较,最终确定了一种更好的方法,从而提高了精度。本地化有助于成功生成区域提案和类标签。最后,提高了图像的分类和定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition Of Fish Categories Using Deep Learning Technique
Nowadays the most crucial aspect in Fisheries is the classification and localization of Fish. Due to Segmentation problems, noise and changes in the environmental conditions, it is difficult to classify the images with good accuracy. However, the object recognition with greater accuracy is in great demand. So, in order to solve this problem, the Convolutional Neural Network is used which helps to classify and localize the images with better accuracy. Fish classification is made by training the dataset and the level of accuracy has been improved by using different activation functions. Several comparisons have been made and finally a better method has been identified, thereby the accuracy is improved. Localization helps to generate Region proposals and class labels successfully. Finally, image classification and localization has been made with greater accuracy.
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