Table Of Contents
In simple words, a machine vision solution can be understood as a kind of technology that enables a computing device to inspect and evaluate still or moving images. With vision systems becoming more powerful and providing wider automation opportunities, it’s no surprise that its demand is rising rapidly. According to Bloomberg, the global machine vision market is poised to reach over $18 billion by 2025.
The success of a machine vision process relies on a carefully balanced mix of elements. A standard machine vision solution includes a digital or analogous camera, embedded systems consisting of processors and software, a frame grabber and illumination systems. Broadly speaking, there are three significant elements in a machine vision process: Material handling, image acquisition and vision inspection software. The success of each element is interrelated with the other two. Let us now dive directly into what each of these elements is.
Material handling refers to the movement, storage and control of products and materials through the process of manufacturing. It incorporates a vast range of manual, semi-automated and fully automated vision system hardware. In the context of the machine vision process, material handling is the process of moving the item and presenting it to the image acquisition system.
Almost all kinds of industries use a variety of material handling equipment and technologies to support the movement of the product across the supply chain. Some prominent ones in machine vision processes are conveyors, industrial robots, AGVs, to name a few.
While designing the material handling infrastructure, it is vital to consider the best practices to ensure that all the vision system hardware and processes in a facility work together in a unified, consistent manner. Some of the good practices are the following:
- Material movement and storage must be well-coordinated throughout all processes.
- Automated material handling should be deployed wherever sensible and feasible to enhance operational efficiency.
- The movement of the conveyor should be appropriately timed with the image capturing part.
- Material handling processes should be simplified by shortening or eliminating unnecessary movements to increase productivity.
Related Article– Image Acquisition Components
Image acquisition can be broadly defined as the action of acquiring an image from some source so that it can be passed over for the subsequent processes. Performing proper image acquisition is crucial in image processing because processing is meaningless without the right image. A typical acquisition system is made up of four significant parts, which have been mentioned below:
The optics and illumination parts ensure that the specific features to be examined are visible and clear. Triggers ensure that the images are captured at the right time. Cameras have a vast range of varieties, with different capabilities. Clearly, the apt type of camera for your vision application depends on the features that you wish to capture. For example, if your application involves the analysis of just the profile of the items, you don’t need a color camera. On the other hand, for features that need color differentiation, you’ll need color cameras. You will need multispectral cameras if you wish to inspect the interior of an item. As is evident, the right camera for you depends on what your machine vision process is trying to accomplish.
Related Article– Image Processing, Size, and Resolution: Machine Vision 2021
Vision Inspection Software
The machine vision inspection software is the engine “under the hood” that aids and drives the imaging part, processing part, and consequently the results. It is the very core of each machine vision application that performs the actual processing and evaluation of the image. Specific software tools are programmed or configured to carry out specific analysis on the pixel-based data on the captured image. Vision inspection software can be divided into three parts:
- Image processing and analysis SDKs
- Graphical environments – For faster application development
- Hardware SDKs – To control cameras and frame grabbers
Now, when it comes to choosing an approach for your vision inspection software, you can choose from options such as rule-based, classic computer vision, algorithms, or other advanced technologies such as Machine Learning, AI and Deep Learning, etc. Rule-based vision inspection software generates pre-determined outputs based on a lot of rules hardcoded into the software using if-else conditional statements.
Machine Learning-based vision inspection software, on the other hand, have no hardcoded set of rules. Instead, they ingest a lot of visual data and come up with rules based on the training samples’ patterns. Also, over time these systems continually update their rules based on the mistakes and thus, increase their accuracy in the long run.
The success of a machine vision solution depends majorly on the individual success of three significant elements, which are material handling, image acquisition, and vision inspection software. In this blog post, we discussed the three elements mentioned above and their importance in a machine vision process.
Get In Touch With Us