AIoT-Cloud-Integrated Smart Livestock Surveillance via Assembling Deep Networks with Considering Robustness and Semantics Availability

Wei Tsung Su, Lin Yi Jiang, O. Tang-Hsuan, Yu Chuan Lin, Min Hsiung Hung, Chao Chun Chen

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this letter, we propose a novel smart livestock surveillance system through cooperation of AIoT (artificial intelligence of things) devices and the cloud computing platform, aiming at providing semantic information via assembling deep networks with AIoT devices of limited resource. The key of the proposed system includes two designs: Deep-net assembling as a semantic surveillance service and the expandable-convolutional-block neural network (ECB-Net). The first is a development architecture of the divide-and-conquer philosophy for establishing semantic surveillance systems, and this work provides a concrete instance for promoting deep-net assembling to livestock industries. The second is an AIoT device-friendly neural network for filtering insignificant camera images to achieve high robustness of smart surveillance systems. The technical details from the architecture design to optimal ECB-Net model creation are presented in related sections. Finally, we develop the prototype of the smart livestock surveillance system and deploy it by swine rooms for conducting real-world integrated tests. Testing results reveal the superior performance of our proposed smart livestock surveillance scheme.

Original languageEnglish
Article number9460764
Pages (from-to)6140-6147
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
Publication statusPublished - 2021 Oct

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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