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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Wisnu Ardhi, Ovide Decroly
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Computer Science & IT Engineering

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Enhanced you only look once approach for automatic phytoplankton identification Wisnu Ardhi, Ovide Decroly; Retnaningsih Soeprobowati, Tri; Adi, Kusworo; Prakasa, Esa; Rachman, Arief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3426-3436

Abstract

Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.
Co-Authors Arief Rachman Esa Prakasa, Esa Kusworo Adi Retnaningsih Soeprobowati, Tri