Semantic Models for the Identification of Laboratory Equipment (SMILE)

Military operators must identify and catalogue the equipment they find when inspecting laboratory facilities. This information is used to determine the lab’s capabilities, including the lab’s potential for building weapons of mass destruction. Currently, operators use computer vision algorithms to help them classify equipment in pictures of laboratory environments. Unfortunately, current image processing algorithms have several shortcomings that prevent them from achieving accurate results in this domain. Charles River Analytics and the University of Wisconsin-Madison propose to design and develop Semantic Models for the Identification of Laboratory Equipment (SMILE). The SMILE framework combines state of the art Convolutional Neural Network (CNN) object detection with a semantic model of laboratory capability to detect and classify the laboratory equipment in an image and to provide an estimate of the laboratory’s capability.