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http://dx.doi.org/10.5392/JKCA.2021.21.01.552

Deep Learning based Fish Object Detection and Tracking for Smart Aqua Farm  

Shin, Younghak (목포대학교 컴퓨터공학과)
Choi, Jeong Hyeon (목포대학교 컴퓨터공학과)
Choi, Han Suk (목포대학교 컴퓨터공학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.1, 2021 , pp. 552-560 More about this Journal
Abstract
Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.
Keywords
Smart Aqua Farm; Deep Learning; Object Detection; Path Tracking; Image Augmentation;
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Times Cited By KSCI : 1  (Citation Analysis)
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