基于卷积神经网络的莱特湾鱼类物种检测应用

G. G. Dialogo, L. Feliscuzo, Elmer A. Maravillas
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引用次数: 3

摘要

本研究提出了一种应用程序,该应用程序采用机器学习算法来识别在莱特湾发现的鱼类。它旨在帮助学生和海洋科学家进行识别和数据收集。该应用程序支持467种鱼类,其中6,918张鱼类图像用于训练、验证和测试生成的模型。该模型总共训练了4000个epoch。使用卷积神经网络(CNN)算法,在epoch 3,661观察到训练过程中的最佳模型,准确率为96.49%,损失值为0.1359。验证时的损失值为1.868,准确度为80.58%。结果表明,该模型能较好地预测马拉丁木和树液的种类,精度达到100%。然而,由于其与其他物种的特征相似性,在测试结果达到55%的准确率后,模型有时会被错误分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network
This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.
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来源期刊
CiteScore
1.60
自引率
0.00%
发文量
12
审稿时长
18 weeks
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