An Image Processing based Object Counting Approach for Machine Vision Application

TL;DR

This paper presents a real-time machine vision system that uses image processing techniques like Otsu thresholding and Hough transformation for automatic, product-independent object counting on production lines, ensuring high accuracy and speed.

Contribution

The paper introduces a novel, real-time, product-agnostic object counting method using simple image processing algorithms suitable for industrial applications.

Findings

Fast and accurate counting results

Independent of product type and color

Reliable performance in real experimental setup

Abstract

Machine vision applications are low cost and high precision measurement systems which are frequently used in production lines. With these systems that provide contactless control and measurement, production facilities are able to reach high production numbers without errors. Machine vision operations such as product counting, error control, dimension measurement can be performed through a camera. In this paper, a machine vision application is proposed, which can perform object-independent product counting. The proposed approach is based on Otsu thresholding and Hough transformation and performs automatic counting independently of product type and color. Basically one camera is used in the system. Through this camera, an image of the products passing through a conveyor is taken and various image processing algorithms are applied to these images. In this approach using images obtained from a real experimental setup, a real-time machine vision application was installed. As a result of the experimental studies performed, it has been determined that the proposed approach gives fast, accurate and reliable results.