SBC 컴퓨팅 환경에서 딥러닝을 이용한 자돈 압사 인식 성능 분석

In this study, we aim to validate the use of Artificial Intelligence of Things (AIoT) for deep learning-based detection of piglet crushing incidents by sows in pig farms, a leading cause of piglet mortality. To achieve this, we developed a deep neural network based on the You Only Look Once (YOLO) m...

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Vydáno v:한국정보통신학회논문지 Ročník 28; číslo 8; s. 1004 - 1007
Hlavní autoři: Taeyong Yun(윤태용), Yeseong Kang(강예성), Woongsup Lee(이웅섭)
Médium: Journal Article
Jazyk:korejština
Vydáno: 한국정보통신학회 01.08.2024
Témata:
전자/정보통신공학
YOLO
Deep learning
Smart pig farm
AIoT
SBC
ISSN:2234-4772, 2288-4165
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Popis
Shrnutí:In this study, we aim to validate the use of Artificial Intelligence of Things (AIoT) for deep learning-based detection of piglet crushing incidents by sows in pig farms, a leading cause of piglet mortality. To achieve this, we developed a deep neural network based on the You Only Look Once (YOLO) model, which was trained to detect piglet crushing events in real-time. We then optimized the weights of the YOLO model using TensorFlow Lite, TensorRT, and edge TPU to ensure efficient operation on AIoT devices with limited computational resources. Finally, we compared the performance of the deep learning-based piglet crushing detection algorithm on various single-board computers (SBCs), including the Jetson Nano, Raspberry Pi, and Coral Dev Board, to validate its suitability for real-world applications. KCI Citation Count: 0
Bibliografie:http://jkiice.org
ISSN:2234-4772
2288-4165
DOI:10.6109/jkiice.2024.28.8.1004