Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection

Authors

DOI:

10.33395/sinkron.v8i4.13130

Keywords:

Computer Vision, Detect Maritime Fish Objects, Deep Learning, Inception ResNet model, Xception models

Abstract

Within the domain of deep learning applied to computer vision, there exists a significant emphasis on the competition between two prominent models, namely Inception ResNet and Xception, particularly in the field of marine fish object detection. The present study conducted a comparative analysis of two advanced neural network architectures in order to assess their efficacy in the identification and localization of marine fish species in underwater images. The two models underwent a rigorous evaluation, utilizing their capabilities in feature extraction. The findings indicate a complex performance landscape, wherein Inception ResNet exhibits remarkable accuracy in identifying marine fish objects, while Xception demonstrates superior computational efficiency. The present study elucidates the inherent trade-off between precision and computational expenditure, offering valuable perspectives on the pragmatic ramifications of choosing one model over another. Furthermore, this research underscores the significance of carefully choosing a suitable model that aligns with the particular requirements of object detection applications in the context of marine fish. This study endeavors to guide professionals and scholars in marine biology and computer vision, enabling them to make well-informed choices when utilizing deep learning techniques to detect maritime fish objects in underwater settings. The research specifically focuses on the comparison between Inception ResNet and Xception models.

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How to Cite

Hindarto, D. (2023). Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2819-2826. https://doi.org/10.33395/sinkron.v8i4.13130

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

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Articles

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Copyright (c) 2023 Djarot Hindarto

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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