Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Shrimp Quality Detection Method Based on YOLOv4

  • Tao, Xingyi (School of Management, Liaoning university of International Business and Economics) ;
  • Feng, Yiran (Dept. of Information and Communication Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information and Communication Engineering, Tongmyong University) ;
  • Tao, Xueheng (School of Mechanical Engineering and Automation, Dalian Polytechnic University)
  • Received : 2022.06.22
  • Accepted : 2022.07.18
  • Published : 2022.07.31
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Abstract

A shrimp quality detection model using YOLOv4 deep learning algorithm is designed, which is superior in terms of network architecture, data processing and feature extraction. The shrimp images were taken and data expanded on their own, the LableImage platform was used for data annotation, and the network model was trained under the Darknet framework. Through comparison, the final performance of the model was all higher than other common target detection models, and its detection accuracy reached 93.7% with an average detection time of 47 ms, indicating that the method can effectively detect the quality of shrimp in the production process.

Keywords

Acknowledgement

This work was supported by the Dalian Young Stars of Science and Technology Project (NO.2021RQ088)

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