An Investigation of Parameter Optimization in Fingerling Counting Problems

Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI:10.5753/wvc.2021.18881

Adair da Silva Oliveira Junior, M. Pache, Fábio Prestes Cesar Rezende, D. Sant’Ana, V. Weber, Gilberto Astolfi, F. Weber, G. Menezes, Gabriel Kirsten Menezes, Pedro Lucas França Albuquerque, Celso Soares Costa, Vanir Garcia, Eduardo Quirino Arguelho de Queiroz, João Victor Araújo Rozales, M. Ferreira, M. Naka, H. Pistori

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Abstract

The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.

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鱼种计数问题中参数优化的研究

本文的目的是研究鱼种计数软件的哪种参数组合导致最小的平均绝对误差(MAE)和最小的均方根误差(RMSE)。为此，创建了一个名为FISHCV155V的图像数据集，并将其分为训练集和测试集，其中测试了软件的不同参数组合。从获得的结果中提取每个参数组合的单个性能指标，如MAE，均方误差(MSE)和RMSE。对视频帧进行了分析，比较了得到最佳和最差结果的参数组合，探讨了这些参数对软件性能的影响。结果表明，最佳组合MAE为5.99,RMSE为9.96。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Anais do XVII Workshop de Visão Computacional (WVC 2021)

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