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

Abstract

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.