A computer vision pipeline for automatic large-scale inventory tracking

Abstract

Monitoring and tracking inventory is one of the most important aspects of administrating any large-scale enterprise operation that involves physical goods. One of the most evident examples of such operations is automotive manufacturing, especially for servicing a global customer base. We present a software solution of Intelligent Process Automation (IPA) that utilizes state-of-the-art computer vision (CV) and other algorithmic techniques to locate, detect, and manage inventory storage logistics using label information from simple warehouse images. When used in conjunction with a recently developed robotic imaging system, our pipeline can be shown to replace the need for costly, error-prone human input to the inventory tracking system. This paper outlines the technical and practical application of IPA fueled by deep learning. The specific motivation for this project was to address a critical need of Mercedes-Benz U.S. International (MBUSI), but the techniques could be applied more generally to other inventory management contexts. We also discuss how our pipeline produces an inexpensive, efficient, and generalizable solution that provides the capability to retrieve data from an unpredictable environment, in contrast to previous approaches.