MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI

TL;DR

The paper introduces MINSU, a computer vision-based system for inventory management that detects, segments, and estimates the fullness of cabinet contents through a multi-step image analysis process.

Contribution

It presents a novel five-step algorithm combining object detection, foreground subtraction, K-means clustering, and percentage estimation for inventory assessment.

Findings

Effective in estimating residual quantities in cabinets

Improves accuracy of inventory counting using image analysis

Potential to automate residual quantity management

Abstract

The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the computational vision analysis method to record the residual quantity/fullness of the cabinet. To do so, it goes through a five-step method: object detection, foreground subtraction, K-means clustering, percentage estimation, and counting. The input image goes through the object detection method to analyze the specific position of the cabinets in terms of coordinates. After doing so, it goes through the foreground subtraction method to make the image more focus-able to the cabinet itself by removing the background (some manual work may have to be done such as selecting the parts that were not grab cut by the algorithm). In the K-means clustering method, the multi-colored image turns into a 3 colored monotonous image for quicker and more accurate analysis. At last, the image goes through percentage estimation and counting. In these two methods, the proportion that the material inside the cabinet is found in percentage which then is used to approximate the number of materials inside. Had this project been successful, the residual quantity management could solve the problem addressed earlier in the introduction.