A Computer Vision-Based System for Metal Sheet Pick Counting

Abstract

Inventory counting is crucial to manufacturing industries in terms of inventory management, production, and procurement planning. Many companies currently require workers to manually count and track the status of materials, which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees. This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material. The type of material of interest is metal sheet, whose shape is simple, a large rectangular shape, yet difficult to detect. The use of computer vision technology can reduce the costs incurred from the loss of high-value materials, eliminate repetitive work requirements for skilled labor, and reduce human error. A computer vision system is proposed and tested on a metal sheet picking process for multiple metal sheet stacks in the storage area by using one video camera. Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83% and a recall of 100%.