Machine Vision systems are finding increased traction in various fields, especially in the realm of industrial automation. They use image capture and processing technologies for visual inspection and monitoring and involve a number of engineering disciplines including optics, computer science, industrial automation and mechanical engineering.
The following is a block diagram of a typical machine vision system:
By definition, any system which involves inputs, processing and output qualify as a machine vision system. In this, inputs are the objects being analyzed/tracked under controlled conditions. The processing mainly involves image processing and vision-based algorithms and outputs typically are the analysis of the captured images and decision-making parameters based on that.
With the huge adoption of automation across industries, the importance of having a fully automated inspection and monitoring system has grown manifold. In addition, drone-based monitoring and robotics are fields where machine vision along with machine learning has been adding tremendous value.
The primary reasons that substantiate the longevity of this trend are as follows:
Though machine vision has multitudes of applications across industries, a few important ones are listed below:
Other industries which are fast catching up are agriculture, transit security and
Machine vision, despite its numerous business, benefits still remains to be utilized to its best potential. The current hurdles facing are:
Implementing a machine vision system requires technology skills in camera design and building automation solutions, embedded design, algorithm development and mechanical engineering, Considering the exponential growth potential and at the same time, understanding the above challenges it is really important to have an expert innovation partner to venture into this space further. At eInfochips, we have implemented machine vision systems for our clients across various industries. For more information, email us at email@example.com