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Ardhi, Ovide Decroly Wisnu
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Enhanced U-Net models with encoder and augmentation for phytoplankton segmentation Ardhi, Ovide Decroly Wisnu; Soeprobowati, Tri Retnaningsih; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1009-1018

Abstract

This study comprehensively analyzes U-Net models for semantic segmentation in phytoplankton image recognition, leveraging encoders such as EfficientNet-B5, MobileNetV2, ResNet50, and ResNeXt50 and employing the Adam optimizer. The research highlights the U-Net MobileNetV2 model with optical distortion, which achieves notable test scores with 93.69% Dice, 88.14% intersection over union (IoU), 99.89% Precision, and 100% Recall, underscoring the efficacy of the applied augmentation strategies, including geometric and distortion transforms, and color and blur techniques. The U-Net ResNet50 model with mix transform consistently demonstrates high accuracy in critical metrics, outperforming others, while EfficientNet-B5 with blur suggests increased model sensitivity with improved recall. These results underscore the crucial role of encoder-augmentation synergy in model performance. Training and testing times across models have remained under 250 seconds, reflecting methodological efficiency. Overall, these results demonstrate the model's excellent performance for the semantic segmentation task.
Co-Authors Arief Rachman Esa Prakasa, Esa Kusworo Adi Tri Retnaningsih Soeprobowati