A Comparison of YOLO and Mask R-CNN for Segmenting Head and Tail of Fish

Prasetyo, Eko and Suciati, Nanik and Fatichah, Chastine (2020) A Comparison of YOLO and Mask R-CNN for Segmenting Head and Tail of Fish. International Conference on Informatics and Computational Sciences (ICICoS). pp. 1-16. ISSN 978 1 7281 9526 1

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Abstract

Looking back on our history, human being has undergone several stages of society. Society 1.0 was denoted by behaviour of hunting and gathering while living in harmony with nature. Society 2.0 formed an agricultural cultivation-based civilization, progressing towards community development. Society 3.0 promoted mass production through industrialization. Society 4.0 was based on invention of computers and internet, allowing distribution of mass information without borders. Now, we are living in Society 5.0 where IoT, artificial intelligence, robotics, and big data are utilized to establish smart societies across the globe. One particular technology commonly found in a smart society is public display. In the post-pandemic era, public display plays an important role to control Covid-19 by presenting educational information in hospitals, health centers, supermarkets, airports, and other public facilities. Developing an informative public display while maintaining high standards of sanitation to avoid spreading of Covid-19 posses a challenging interaction circumstances. In this keynote lecture, we provide a case study on implementing artificial intelligence and smart sensor to develop a touchless smart public display. We have implemented uncalibrated eye tracking technique for spontaneous interaction—without direct interaction with high contact surface of the display. Our technology provides an alternative interaction modality to support future smart societies.

Item Type:	Article
Uncontrolled Keywords:	segmentation, object detection, YOLO, Mask R-CNN, fish freshness, head and tail of fish
Subjects:	Technology
Divisions:	Faculty of Engineering
Depositing User:	Perpus Ubhara Surabaya
Date Deposited:	24 Jun 2024 06:36
Last Modified:	24 Jun 2024 06:36
URI:	http://eprints.ubhara.ac.id/id/eprint/2550

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