Abstract:
A Technology involves, and computational power becomes available in many
embedded systems such as cameras to provide high-quality images and high-speed
image processing, computer vision-based object recognition and identification
technologies have become useful in solving many computer-based research
problems. One such interesting problem is inventory control and management of
parts that are not readily identifiable with a part number, product code or an item
name as is the conventional scenario. The range of models, components and parts
that are available in an embedded systems laboratory of a computer engineering
institution or company is quite extensive and many of these units tend to look similar
and could be difficult to identify through a simple visual inspection. A smart
inventory management system with the ability to intelligently identify different
electronic and computing parts and components will be a useful addition to an
embedded systems laboratory. This research thesis proposes a computer vision-based
methodology for a smart inventory management system for an embedded systems
laboratory to recognize equipment and features of equipment (device name, type,
serial numbers, identification marks, and manufacturer details) for the people who
are entrusted with keeping and issuing such devices in a laboratory. The focus of the
research is to implement the inventory management system with a database of
equipment‟s, which can be used in issuing and storing the equipment without while
minimizing product identification errors when human expertise is not readily
availab
