Applying Image Recognition and Tracking Methods for Fish Physiology Detection Based on a Visual Sensor

Jia Ming Liang*, Shashank Mishra, Yu Lin Cheng

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

3 Scopus citations

Abstract

The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish’s physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes.

Original languageEnglish
Article number5545
JournalSensors
Volume22
Issue number15
DOIs
StatePublished - 08 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • Internet of Things
  • correction mechanism
  • image recognition
  • object tracking

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