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Combining Autoencoder and Yolov6 Model for Classification and Disease Detection in Chickens

Authors:

Khang Hoang Nguyen,

Huynh Vu Nhu Nguyen,

Hoang Ngoc Tran,

Luyl-Da QuachAuthors Info & Claims

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

Pages 132 - 138

https://doi.org/10.1145/3591569.3591591

Published: 13 July 2023 Publication History

Abstract

Among the causes of reduced production is a chicken disease, which can negatively affect consumer health. With the advancement of computer vision technology and profound innovations in the field of research, it has become increasingly important to analyze disease images collected by sensors in chickens to analyze the possibility of infection conveniently and efficiently. Consequently, research proposes to identify lesions using the Autoencoder and Yolov6 model to classify and detect diseases in chicken flocks. This model is suitable for different chicken breeds from many countries and regions. This method helps improve and enhance image recognition accuracy by incorporating the data enhancement method in the data preprocessing step. The results show that the value of val/mAP (average accuracy) obtained by the method proposed in this paper is 99.15%. Moreover, hit over 90% on the test dataset. This method can be applied to the early detection of disease-carrying chickens in the captive population, ensuring a quality food source for humans.

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Cited By

Quach LQuoc KQuynh ANgoc HThai-Nghe N(2024)Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++IEEE Access10.1109/ACCESS.2024.335180512(9719-9737)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351805

Index Terms

Combining Autoencoder and Yolov6 Model for Classification and Disease Detection in Chickens
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

February 2023

310 pages

ISBN:9781450399616

DOI:10.1145/3591569

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 July 2023

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ICIIT 2023

ICIIT 2023: 2023 8th International Conference on Intelligent Information Technology

February 24 - 26, 2023

Da Nang, Vietnam

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Cited By

Quach LQuoc KQuynh ANgoc HThai-Nghe N(2024)Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++IEEE Access10.1109/ACCESS.2024.335180512(9719-9737)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351805

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