Fish Detection and Classification using YOLOv8 for Automated Sorting Systems

Ari Kuswantori; Dwi Joko Suroso

doi:10.24014/ijaidm.v7i2.30967

Fish Detection and Classification using YOLOv8 for Automated Sorting Systems

Ari Kuswantori, Dwi Joko Suroso

Abstract

Automation plays a crucial role in scaling up freshwater fish cultivation to address the future threat of food scarcity and meet growing nutrition needs. The fish industry, in particular, develops automation in the sorting and selection processes. However, research on this technology's development is still very limited. In this work, we propose an approach for detecting and classifying fish running on conveyors. We use YOLOv8, which is the most popular and newest deep learning model for object detection and classification. We conducted our test using the KMITLFish dataset, a moving conveyor belt recording that encompasses common cultivated freshwater fish in Thailand along with some endemic species. As a result, our proposed method was able to accurately detect and classify eight types of fish at a conveyor speed of 505.08 m/h. Moreover, we developed this work using a ready-to-use AI platform, intending to directly contribute to the advancement of automatic fish sorting system technology in the fish industry.

Keywords

Automatic Fish Sorting; Deep Learning; Fish Classification; Fish Harvest Technology, YOLOv8

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.30967

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