Via vision, we receive 80% of all the information. Machine vision ( or computer vision) is also the fastest way for any company to reach its goal.

Choosing the right partner is a big decision. In 1985, Apple’s directors let Steve Jobs go. And in 1996, they invited him back. The company was close to bankruptcy and Steve Jobs was having a successful career in Disney.  And in a few years, they became profitable again. That was the time when the world saw iPhones and iPads. Obviously, Apple’s board made the right choice.

What is Machine Vision?

Large supermarkets like Walmart and Target buy on a regular basis a considerable number of barcode scanners with simple Machine Vision (MV). This is just one of the examples. In reality, machine vision is applied anywhere where visual data is accumulated with the help of cameras.

Laboratories, governments, big retail stores, production lines: each business or governmental institution can improve the level of operations with Machine Vision today.

Machine vision(MV) and computer vision (CV) are often used as synonyms. Thus, MV can promptly differentiate common images and automate choices. It can choose between yes or no, green or red, correct numbers or wrong numbers, quality or defect. The application of MV will vary from medical image analysis to intrusion prevention.

Criteria for Choosing Machine Vision Companies

MarketsandMarkets assures that the CAGR of the global MV market will make up 6.1% in 2020-2025 with a total of US $13B. That indicates a rather stable demand for MV devices during these 5 years.

If an entity is considering the employment of MV they should regard the criteria for choosing MV solutions companies. There are mainly 2 criteria and both are equally important for a new successful MV project.

+Past positive reviews of clients on AI projects on independent rating platforms. Here is the example of one of our past client’s review:

+Technological stack of the company in terms of AI (Computer Vision, Machine Learning, Deep Learning, Natural Language Processing, etc.). The thing is most likely your solution will need to apply more than one AI technology.

The machine vision company you choose as the partner must have engineers who know modern frameworks, libraries, solved similar problems in the past, and can respond to the challenge adequately. In other words, it is crucial to know that the company has the tools to cope with a project of any complexity, and it has a positive experience of doing it.

Positive Reviews from Clients: Platforms

To evaluate the performance of machine vision companies, one can visit specialized platforms such as Clutch, GoodFirms, DesignRush, TheManifest, and similar. They allow clients to publish a review of their joint projects with developer companies. 

There, one can find evaluations in terms of quality, services, schedule, duration, and cost of the projects. 
As well, one can learn more about the project’s summary, what kind of tasks it solved, what methods the company used, and what results they achieved.  Also, it is easier to anticipate what types of relations the company can offer based on the feedback summary of their clients. 

This information can make a ground for decision-making whether or not to make the first step and contact an MV solutions company. Or it can allow for learning more information.

Machine Vision Companies

Machine Vision Technology Stack

The companies that develop MV solutions operate with special languages, frameworks, and libraries. The most popular technologies are the following.

Python
This is a popular language for analytical tasks and operations with data. Developer companies use it to create ML software in a comprehensive and rather errorless manner.

This programming language for MC offers a tray of ML techniques. With the help of R, you can visualize data, sample it, and analyze it.
Java
Java Virtual Machine is the best tool for ML if you need to write the same code on many platforms. If the speed of performance is a priority, the company needs to have expertise with this machine.
TensorFlow
Google’s TensorFlow framework performs numerical computations for ML. It has become very popular because it offers APIs for many popular languages such as Python, Java, C, C++, Rust, and other languages.
PyTorch 
This Facebook’s framework is very smart. It can work with DL and create easy-to-process graphs. Python developers use it as the #1 tool if they need to halt the interpreter and debug their application.
Keras
Keras is an open-source library that works with TensorFlow. It offers APIs that allow for frameworks that work with neural network modules.
OpenCV
This rich library for CV can detect and segment different images, faces, and objects. As the most popular image library, it scales, translates, rotates, and resized images. 
ScyFi
This library is meant for multi-dimensional work with images processing on Numpy arrays. It is handy for convolution and blurring images.
Scikit image
This Python library works with images and also has parts in Cython. It can segment images, change their forms, color, detect features, and filter.
PIL (Python image library)
This library, as it reads, needs Python language. It can open, save, and work with many image formats, and is the best in image enhancement.
Panda
Open-source library for data analysis and visualization.
NumPy
Open-source library for complex mathematical operations.

Make sure the company you are about to hire as a vendor has experience with the mentioned above languages, frameworks, and libraries.

Machine Vision  Functions

Machine Vision  Functions

Object Detection

MV can detect and locate objects in an image or video.  Thus, MV algorithms can be requested to recognize objects, count them, identify their location, and track them.

Note. How can machines detect objects? They label objects, look at their colors and edge pixels to see what may belong to an object, and then box them. 

Facial Recognition

Viola-Jones algorithm is a generally accepted way to reach a high detection rate preserving a high speed of processing. When cameras gaze directly into somebody’s face, most probably, they are doing 4 actions. They select, evaluate, create a classifier, and cascading classifiers. Then, they can, for example, identify a common customer in the shop.

Motion Tracking 

Tracking of movement is a challenge in MV. The algorithm has to locate an object in all frames of a video giving only its location in the first frame. In this way, MV can detect strangers by the vehicles, or fleet, and buyers by the shelves.

Sentiment Detection

Marketers seek customers’ feedback regarding the prices, assortment, product availability, and general client satisfaction.  Learning their sentiments can guide marketers in their further work and impact revenues. That is why the shop’s MV cameras can be the open gate to this information. 

Applications of Machine Vision in Business

Optical Character Detection

In logistics and transportation, Optical Character Recognition and Azure Cognitive Services allow for filling in thousands of documents. They raise 4 times the procession of documentation. OCR can define printed and handwritten text and transform it into electronic format. 

Optical Character Detection

Detect Suspicious Behavior Around Vehicles

Video analytics solutions are very popular with fleet management businesses for security reasons. And the motion tracking function of MV is the core of these solutions. They can detect restricted objects or suspect individuals, detect the restricted behavior, and track them.

Detect Suspicious Behavior Around Vehicles

Track Speeding on the Roads

It’s hard to imagine busy roads without traffic cameras. They look for speeding, suspicious objects, and traffic jams. Motion recognition of MV together with license plate detection make speeding less attractive for many drivers. Public authorities show great interest in traffic cameras and MV because they are effective. 

Track Speeding on the Roads

Charge Customers After they Have Left the Store

Machine vision use cases for retail are hard to count. Some customers prefer not standing in lines at shops. It’s been a while since Americans got the technology to charge their customers after they have left the store. 

  1. The Amazon application in such stores can scan the products in a buyer’s basket with the MV function.
  2. It will recognize the customer’s identity via the face recognition function.
  3. It will charge their credit cards after they have left. 
Charge Customers After they Have Left the Store

In this way, shop owners raise customer satisfaction and loyalty and save many work hours for personnel.

Detect Empty Shelves

Detect Empty Shelves

MV cameras can contribute to detecting empty shelves. It saves the time of shop executives and representatives of producers. Also, they can collect statistical information about types of products and their demand from these cameras. That is why it can be an investment in marketing and logistic analytics. 

Send Ad Messages to Buyers in the Store

Many grocery customers usually shop at nearby stores and pay there with their credit cards. The shops’ cameras know their faces very well. Along with that, marketing teams set geofencing 24/7 and attract nearby buyers on their mobiles. Nevertheless, when it comes to buying, these customers often fail to notice hot propositions, discounts, or novelties of their favorite products. Until now. 

Send Ad Messages to Buyers in the Store

With MV, shop owners can send them ad messages while the customers are in the shop. 

  1. MV can scan the faces of customers that geofencing finds, identify these customers, and see their purchase history. 
  2. Then, the app will select the offers and products that can be interesting to these customers. 
  3. Afterward, it will send them personalized ads on mobiles to drive them directly to the right offers. In this way, MV contributes to product marketing.

Conclusion

Companies and organizations can choose the right MV solution developer company considering at least 2 criteria. First, they can look for positive reviews from clients on specialized platforms such as Clutch and GoodFirms. And second, they can make sure that the company has a convincing MV technology stack. It can encompass Python, TensorFlow, Keras, OpenCV, or similar. The main capability of MV is to analyze images and videos and perform different functions with them. MV makes possible object detection, face recognition, motion tracking, and sentiment detection. Companies and organizations can apply MV in diverse ways. They can automate simple choices or use MV in combination with more complex solutions. With the right contractor, they can track speeding on the roads, quickly fill in thousands of logistic forms, raise revenues by sending out personalized ads in stores, and by charging buyers, not at the counter but after they have left the shop. With MV, one can significantly raise the level of life for customers, enhance positions on the market, and make budget money beneficial for citizens. 

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