Machine vision is the process of acquiring digital images and computing a result from it to automate an industrial process. The traditional inspection process involves a human being physically inspecting an object and looking for defects and faults. The modern inspection process involves machine vision where a camera looks at the same object and identifies defects and faults. Machine vision is a non-contact technology and hence easy to integrate. It is faster than humans as the speed of machine vision ranges between a few microseconds to a few milliseconds. It is also accurate and consistent as there is no subjective bias in the decision-making process. With proper design and implementation, the accuracy given by a machine vision system becomes much higher than any typical human being. This helps in powering up your machine vision system by reduction of manpower and cost savings, consistent product quality for your brand, and traceability of all operations due to data collection and storage by the vision system.
Parts of a machine vision system –
Pre-processing automation –
Pre-processing automation is also known as material handling automation. This deals with the different tasks that are performed to help the machine vision system in the process of image acquisition. The use of conveyor belts, manually transferring the object to the camera’s field of view, pick and place systems and robotic camera movements are all such types of pre-processing activities that are done to make the most out of the image acquisition process. Image acquisition can start immediately when pre-processing steps are done correctly.
Image Acquisition –
Image acquisition is the point where the camera captures the image through an automated trigger. The quality of the image is a very important factor that can make or break your machine vision system.
Components of image acquisition –
- Trigger –A trigger is a switch that makes the image acquisition system or camera capture the image of what is in front of it.
- Camera –Industrial cameras are used in MV systems that are rugged and specialized.
- Optics –The lens of the camera affects the quality of the image and must be chosen carefully.
- Illumination –The lighting must be consistent and critical for image acquisition.
Image Processing –
There are two kinds of approaches towards image processing –
- Rule-based system –In a rule-based system, the programmer defines the rules that have to be used to achieve the desired result. These predefined rules are fed into the system and image processing happens only based on these rules.
- AI-based system –In an AI-based system, the algorithms are “taught” with help of examples. The algorithm figures out the correct predictions in unknown datasets using the examples that were fed to it through the known datasets.
In the AI-based system, image processing is further divided into three steps –
- Data Preparation –Data preparation refers to the annotation or labeling of the training data so that the machine vision system can learn the distinct objects from various images that it needs to detect and identify. Correct data labeling can make a machine vision system learn more accurately.
- ML/DL Training –The system uses a closed-loop training algorithm to learn the particular task at hand. The predictions made by the MV model are constantly subjected to feedback and evaluation so that machine vision can learn and improve its accuracy over time.
- Data Inference –Data inference is the final step of the image processing system where the machine vision system is given an object from an unknown dataset and the vision system is required to predict an action or identify the object. If the training is done correctly, the data inference made by the machine vision system will be accurate.
Qualitas Preferred Partner Program –
Qualitas was established in 2008 and has currently established itself as a leader in visual inspection technology for industrial automation. The core expertise of Qualitas lies in the domain of machine vision and AI. Qualitas Preferred Partner Program can be accessed through four simple steps –
- Partner Evaluation –A simple evaluation to make sure that Qualitas is the right fit for the partner’s requirements and needs.
- Signed Partner Agreement –An agreement is then signed between Qualitas and the partner.
- Demo Equipment Procurement – The equipment that needs to be integrated with machine vision technology has to be procured to show the customers.
- Training –Training in the fields of image acquisition, requirement analysis, machine learning, and AI is provided.
The Qualitas Preferred Partner Program gives partners qualified leads to various leading industry experts. Constant seminars, webinars, and interactive sessions are held from time to time with the industry leaders. It provides specialized and exclusive training for various technologies relevant to machine vision like deep learning and AI. It also teaches project management and business analysis. Joining the preferred partner program will also ensure access to discounted hardware and technical support. Any machine vision technology integrated into the partner’s hardware will be provided with proper documentation and templates. The Qualitas EagleEye cloud platform can also be subscribed to through this partner program with great discounts. Various discounted subscriptions for inference licenses can also be availed through this program.
Thus, to power through the relatively new but extremely efficient field of machine vision, a basic understanding of the model is required. Machine vision is broadly divided into 3 units, and modern machine vision technology uses neural networks and deep learning algorithms to aid the overall design of the model. If your business is unfamiliar with the proper utilization of machine vision systems, the Qualitas Preferred Partner Program can assist you in making the most out of your equipment through machine vision. Using the Qualitas EagleEye Cloud Platform, all kinds of machine vision operations can be performed with a simple plug-and-play system that does not require you to dive into the complexity of algorithms and just reap the benefits of the machine vision system.