Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection

Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection

This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed. The hyperparameter in the feature-extraction phase...

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Bibliographic Details
Main Authors: Iza Sazanita Isa, Mohamed Syazwan Asyraf Rosli, Umi Kalsom Yusof, Mohd Ikmal Fitri Maruzuki, Siti Noraini Sulaiman
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Image processing speed
object recognition
optimization model
tuning hyper-parameter
underwater imaging
Online Access:https://ieeexplore.ieee.org/document/9773108/

Internet

https://ieeexplore.ieee.org/document/9773108/

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