Machine vision or computer vision is an emergent technology that allows computers to extract meaningful information from visual inputs like digital images, videos, and so on. A computer or any other software system can take actions or make recommendations by utilizing the information extracted from the digital images. Machine Vision acts like the “eyes” of a computer system while Artificial Intelligence acts like the “brain”. Together, they have a wide range of applications and use cases. In manufacturing, for instance, machine vision can be used to maintain the quality of products, detect errors, identify text from packaging, count objects, etc. The market for machine vision is huge and growing. It will reach a size of 48.6 billion USD by 2022.
How does Machine Vision work?
Machine Vision uses sensors and cameras for visual input and software training algorithms to extract information from that input. The output is a verdict or an action. Machine vision uses data as fuel. The data is analyzed by the algorithm repeatedly until it successfully identifies data based on their similarities. For instance, a vision model learning to identify defects in automobile tires needs to identify tires that satisfy the criterion and tires that have defects and must be rejected and also distinguish between the two of them. This is possible with the help of deep learning and convoluted neural networks.
What are some applications of Machine Vision?
- Image classification – Image classification refers to the identification and categorization of images into certain groups or classes. For example, the machine vision system is required to identify the image of a bearing and also understand that the bearing belongs to a gear chain.
- Object Detection – Machine vision can detect objects with the help of its image classification abilities and thus detect damages in the assembly line without any manual intervention.
- Object tracking – Tracking of an object is performed after object identification and detection is done. With the help of sequential images and video capturing techniques, an object can be tracked in real-time.
Also, Read 3 Uncommon Applications of Machine Vision
What is included in the fixed costs of the machine vision system?
The cost of hardware is primarily fixed. It includes essential parts of a machine vision system like cameras, proper lighting, adequate software handling systems, and supporting architecture. These parts are pivotal to the machine vision system and the usage of the machine vision system will not be possible if any of the parts are missing. According to your requirements, you can choose the specifics of such parts which come in a varying range of prices.
What equipment costs do you need to look into?
- Cameras – Camera uses for taking images of objects in a machine vision system are called CCD cameras. Over time, these cameras have become lighter, cheaper, and smaller. The images produced by these cameras are sharp and accurate. There is a new range of cameras called dual output cameras that also reproduce images quicker. CCD color cameras are also a new technology that helps the computer vision system to distinguish between the object and the background better, enabling faster identification and detection.
- Frame Grabbers – Frame Grabbers are specialized analog to digital converters. Their work is to convert videos and images into digital information. New generation frame grabbers have greater stability and accuracy by using features like enhancement on fly and image processing.
- PCs – Machine vision systems have become less bulky with the help of personal computers. The input and output requirement of the MV system is handled by today’s generation of personal computers. Such computers are embedded in the hardware that supports the software. They are small and effective. Distributive intelligence in the PCs has also helped to increase factory automation speeds with great effectiveness.
- Software – Software has become more modular over time. Advanced libraries and superior graphic user interfaces have made it possible to develop, design, and maintain machine vision tools via operating systems of personal computers. All the tools and technologies used to support machine vision are also becoming PC compatible. Hence there are no integration issues with such tools and the performance of the system happens seamlessly.
- New technologies – Information and data transfer happens on a fly with high-speed serial data ports like USBs. The capabilities of machine vision systems are enhanced with such technologies. Embedded PCs and powerful digital cameras work in coordination with each other to make the most out of the machine vision system. Real-time video rates in information and data transfer have also been achieved because of such technological developments.
What are the variable costs which you need to take care of?
Variable costs vary based on the size of your company, the manpower it needs, and other such factors. For instance, if your company has skilled engineers, then machine vision customization is easier and more convenient. Your machine vision solution can be designed at a cheaper rate, but your manpower expenses would be high. The costs also might vary based on your business plan and future goals.
What are the maintenance costs of a machine vision system?
Training data in MV systems are stored in the cloud. Upgrades and hardware changes are not very expensive if done periodically. With the integration of AI into machine vision, high computational complexities are also reduced which also optimizes costs.
Thus, a complete machine vision system can be deployed within a few thousand dollars because of the technological optimizations of today. The typical range of a modern machine vision system can be between 5000 to 20000 USD. This includes hardware costs, software costs, computation costs, and storage costs. Counter-intuitively, the usage of AI-based tools and algorithms has reduced the costs of machine vision systems dramatically. Cloud infrastructure and more modular, efficient, and minimalistic technology coupled together have made it possible to achieve this feat.