A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations

Alireza Aghasi (), Arun Rai () and Yusen Xia ()
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Alireza Aghasi: Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331
Arun Rai: Center for Digital Innovation and Computer Information Systems Department, J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303
Yusen Xia: Institute for Insight, J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303

INFORMS Journal on Computing, 2024, vol. 36, issue 2, 616-634

Abstract: Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site.

Keywords: image processing; layer and object counting; machine learning; operational efficiency (search for similar items in EconPapers)
Date: 2024
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