Design of Object Identification System Based on Machine Vision

Abstract

Object sorting is an important aspect in almost all the industries. Production industries like food, chemical, petroleum and textile industries have to sort objects on numerous parameters. Various automated object sorting systems are required to avoid human flaws, with increase in productivity and reduce the overall time. Objective of the present work is to develop a part identification system using machine vision. Due to the advantage of LabVIEW in controlling hardware effectively it is employed in the present work. The Vision camera once identifies an object based on its attributes like color shape and size, immediately a signal should be communicated with the controller for separating that object. In this work the signal is shown as a glowing LED. Also the number of objects of particular category passing on the conveyor is counted and displayed to illustrate moving objects identification. A low speed conveyor belt is fabricated with different test objects passing over it. For identifying colors, wavelength data is used, for identifying the shape geometric pattern matching is used and for identifying the size edge detection is applied. The developed G-programming environment generates a graphic user interface in front panel. Ability to count the objects of specific attribute is tested for different trail runs. Thesis is organized as follows: Chapter 1 contains introduction to machine vision system, its components, the objectives of present study and literature review of similar works. Chapter 2 deals with various methods used in vision and their implementation in LabVIEW as done in this work was presented in chapter 3. Chapter 4 gives brief conclusions and future scope of present work.