Launching a computer vision project is a challenging task. You need to gather a team of specialists with the right tech skills, have a clear strategy in place, and know how to mitigate potential risks. 

The battle for the best computer vision engineers in the global market is hot. It takes time and money to find, hire, and retain the experts you need. So how to partner with a computer vision development company that will help you build an efficient team, choose the right tech stack, and successfully deliver a solution?

In this article, we share our tips and best practices on how to choose a trusted computer vision development company supported by real-life success stories. You will discover how to approach digital transformation with machine learning and computer vision and overcome key challenges.


  1. Why companies invest in computer vision 
  2. Where to find a computer vision development company
  3. How to select a computer vision development company?
  4. Key computer vision challenges your technology partner can help you tackle
  5. Companies that benefited from outsourcing computer vision development
  6. Why choose N-iX as your computer vision development partner?

Why companies invest in computer vision 

The reality is that now machines can visually sense and analyze the world. Autonomous driving, facial recognition, traffic surveillance, 3D environment reconstruction, robot-assisted surgery - all this and much more have become possible with computer vision. 

Businesses are increasingly implementing computer vision to enable new efficiencies, precision, and control. The global computer vision market was valued at $10.6B in 2019 and is expected to reach $19,054.9B by 2027. It is going to grow at a rapid pace, so companies can be safe to invest in it.

The factors that drive the growth of the computer vision market are the rapid adoption of process automation, the surge in demand for vision-guided robotic systems, and favorable government initiatives. The global pandemic has also increased the demand for computer vision solutions. Many well-known giants as well as local start-ups are developing computer vision software to prevent, mitigate, and contain the virus. 

Based on the component, the computer vision market is segmented into hardware and software. The hardware component accounted for more than 70% of the global revenue in 2019. If to consider the product, there are PC-based and smart camera-based computer vision systems. The PC-based computer vision systems segment led the market and accounted for more than 58% of the global revenue in 2019. 

Computer vision is now applied across all industry verticals. It is used in disease diagnoses in healthcare, biometric analysis in security, quality inspection in manufacturing, obstacle detection and guiding autonomous vehicles in transportation, ad recommendations in digital advertising, etc. According to the 2019 McKinsey & Company report, computer vision is widely used in automotive, telecom, high tech, and healthcare industries. If to summarize the applications of computer vision across industries, they fall into the following categories:

  • Quality Assurance & Inspection
  • Positioning & Guidance
  • Measurement
  • Identification
  • Predictive Maintenance
  • 3D Visualization & Interactive 3D Modeling

According to the Forrester research, 64% of global senior business purchase influencers say that computer vision will be very or extremely important to their firm in the coming year. 58% say that their firm is implementing, planning to implement, or interested in implementing computer vision in the coming year. Here is why they are ready to invest billions in computer vision. The benefits of computer vision are numerous in every industry:

  • Time-efficiency
  • Accuracy
  • Reliability
  • Simpler and faster processes
  • Reduced costs
  • Improved customer experience
  • Fewer staff members on the production lines (great value in the pandemic times)

The growing number of use cases and success stories spurs demand for experienced computer vision engineers who are extremely thin on the ground. That’s why many companies, from startups to Fortune 100 leaders, choose to outsource their computer vision development.

Where to find a computer vision development company

The lack of AI skills is one of the major obstacles to implementing different computer vision projects. In the United States, AI and Machine Learning roles grew by 74% annually between 2016-2019 according to LinkedIn. Such a drastic increase in demand has caused a skill gap on the market. The same is true of the UK, 81% of UK businesses say a shortage of talent is the biggest hurdle to AI adoption. Thus, more and more companies are exploring various outsourcing destinations. 

According to the 2019 SkillValue report, Eastern Europe has the largest number of countries holding the top 25 positions in terms of the most skilled tech professionals. Ukraine and other countries of Eastern Europe are home to a wide variety of reliable AI software development companies. According to LinkedIn, the number of experts ready to work in artificial intelligence and machine learning in Ukraine exceeds 30,000. The country has a rich pool of software engineers that specialize in such technologies as Scala and Python (66% of Ukrainian software developers surveyed by DOU learn this technology) that are widely used for Data Science and Machine Learning, as well as Apache Hadoop framework that is used for Big Data engineering.

How to select a computer vision development company?

  1. Compile a list of computer vision development companies you will review in terms of their service quality, expertise, and security standards.
  2. Take into account the reviews, references, video testimonials of the vendors you consider as potential partners. You can find this information on their websites, LinkedIn accounts, ranking platforms (Clutch, Manifest, Goodfirms, etc.);
  3. Consider the size of the vendors and their recruitment capabilities, as mid-size and large providers are more likely to have resources you need internally than smaller companies;
  4. Pay attention to administrative facilities, equipment, and other infrastructure of your potential computer vision development partner;
  5. Make sure your potential outsourcing provider complies with international security standards such as ISO, PCI, HIPAA and has an effective security policy in place; 
  6. Ask your potential vendor the right questions to be sure that the provider is reliable
  7. Ask a vendor to provide a portfolio of successfully delivered computer vision projects.
  8. Assess the skills of computer vision engineers your potential partner employs:
  • 3-5 years of experience with C/C++, Python, Java, .NET;
  • Strong expertise in AI & ML, big data, data science, cloud, DevOps, QA;
  • Versatile technical stack: OpenCV, VisionWorks, PyTorch, TensorFlow, Caffe, SimpleCV, etc.;
  • Proven skills in developing, training, testing, and optimizing models;
  • Experience with techniques for object classification, detection, and segmentation;
  • Profound knowledge of OCR; NLP; CNN; RNN;
  • A strong understanding of supervised, unsupervised, and reinforcement learning.

How your technology partner can help you with computer vision challenges

  1.  Develop a detailed strategy

Choose a vendor who offers Discovery Phase service. Before kicking off the computer vision project, you need to have a detailed strategy in place. An experienced partner will help you estimate TCO and the profitability you will gain in the short term and in the long run. Also, it is important to collect all the project artifacts and produce the deliverables that you need to transition to the implementation phase.

  1. Improve source and training data

For effective analysis, you need to have enough reliable data. When the number of data sets for training a model is insufficient or of poor quality, the result will be inaccurate detection and recognition of images and objects. There are several methods of how to solve the problem:

  • Data augmentation allows you to significantly increase the diversity of data available for training models, without actually collecting new data. The augmentation techniques used in deep learning applications depends on the type of data. To augment plain numerical data, techniques such as SMOTE or SMOTE NC are popular.  For unstructured data such as images and text, the augmentation techniques vary from simple transformations to a neural network generated data, based on the complexity of the application.  
  • Incremental learning is a method of machine learning which does not require a large amount of data for training a model. Instead, learning starts with a very simple model typically predicting the average value with some degree of deviation. When a data scientist enters new data examples, the model is trained to be able to predict more accurate results. Over time, the number of data sets is good enough to make reliable forecasts.
  • Reinforcement learning is one of three basic machine learning techniques alongside supervised learning and unsupervised learning. It uses rewards and punishment as signals for positive and negative behavior. In robotics and industrial automation, RL is used to enable the robot to create an efficient adaptive control system for itself which learns from its own experience and behavior.
  1. Choose the right data storage

When it comes to data, there arises the question which data storage solution to choose: data warehouse or data lake. Data lakes are often used as a part of machine learning or advanced analytics solutions. They are often used in ML projects as they let collect data from multiple sources in real-time and store it in its original format. A data lake is ideal for those who want an in-depth analysis of broad-spectrum data that is gathered over a longer period of time, while a data warehouse is perfect for operational processes and day-to-day activities. However, many companies are now using both storage options, especially when a data warehouse is built upon a data lake, and it uses the data from a DL that has been cleansed and structured. 

  1.  Increase training and testing of models

It is vital to test and train models on volumes of properly annotated images or videos with clearly defined metadata. Without proper testing and training, you risk experiencing a number of irreducible errors as well as bias and variance issues. Thus, you need to split the dataset into two distinct subsets: a training set and a testing set. You typically select 20% of the data records at random and set it aside as the testing set, leaving the remaining 80% of the dataset to train the model. The training set teaches the model on how to predict the target values. The testing set tests the quality of the learning if the model is good at predicting beyond the data is used in the learning process. It will help avoid the bias-variance problem. The key to success is finding the balance between bias and variance. The irreducible error is out of the control of the machine learning engineer who is building the model. It’s an error caused by noise in the data, random variations that don’t represent a real pattern in the data, etc. But bias and variance are inversely correlated -  whenever you lower bias, variance will increase. 

Companies that benefited from outsourcing computer vision development

Case study #1: Driving logistics efficiency with computer vision

Our client (under NDA) is a German-based, Fortune 100 engineering and technology company. To improve the logistics between 400+ warehouses, the client introduced a platform that turned out to be ineffective and unscalable. So, they have chosen N-iX as a computer vision development partner to upgrade and extend their system.

This project consists of three primary components: 

  • Changes in architecture. Migration to microservices architecture allowed us to add new AI-related services: anomaly detection, delivery prediction, route recommendations, object detection in logistics, OCR (optical character recognition) of labels on boxes, Natural Language Processing for document verification, data mining, and sensor data processing.
  • Computer Vision solution. Our client had CV algorithms written by another vendor, which were inefficient and unsuitable for production. Therefore, we found a top-notch CV expert with a Ph.D. degree to run the CV workstream. After careful examination of the existing algorithms, we decided to redevelop them completely. We changed the architecture of the solution and introduced Continuous Delivery for Machine Learning, which allows implementing continuously repeatable cycles of training, testing, deploying, monitoring, and operating the ML models. That is especially important given the global scale at which our client is operating.
  • Multiplatform CV mobile app. Also, our team designed the architecture of the multiplatform Computer Vision mobile app and is responsible for its end-to-end development. The app covers object detection, package damage detection, OCR, and NLP for document processing.

Case study #2: Computer vision solution for effective traffic management

Our client (under NDA) is an Australian-based company that develops intelligent transport solutions (ITS) for government, police, and traffic departments.  The company provides solutions and services to help minimize traffic congestion, lessen emissions, eliminate fatal crashes, and reduce vehicle incidents thus making our cities safer and saving lives.

The client needed a reliable software development partner with proven experience in providing computer vision development services to build an intelligent transport solution. They looked for a company with an Agile mindset and versatile expertise in Python, Data Science, Deep Learning, etc.

Here at N-iX, we collaborate with the client’s team on a solution that monitors traffic on the road. Together with the client, our experts have been working on several tasks: 

  • Windshield detection
  • Person detection
  • Seat-belt classification

With the help of Computer Vision and Deep Learning, the solution can identify an offender behind the wheel. It detects whether a driver has fastened the seat belt or not. Also, it captures distracted driving behaviors, which divert attention from driving, including talking or texting on your phone, eating and drinking, talking to people in your vehicle, etc. It has automatic Number Plate Recognition (ANPR) and Artificial Intelligence (AI) recognition of vehicles. This allows creating fines in real-time based on captured data.

Why choose N-iX as your computer vision development partner?

  • N-iX is trusted in the global tech market: the company has been listed among the top software development providers by Clutch, in the Global Outsourcing 100 by IAOP for 4 consecutive years, recognized by GSA UK 2019 Awards, included in top software development companies by, and others.
  • A pool of 1,000+ experts that have experience in providing computer vision development services for businesses of different sizes.
  • A team of 70+ data analytics specialists.
  • Expertise in the most relevant tech stack for implementing computer vision solutions including Data Science, AI/Machine Learning, BI, DevOps, Cloud, C/C++, Python, Java, NET.
  • N-iX is a Select AWS Consulting Partner, a Microsoft Gold Certified Partner, a Google Cloud Partner, and an OpenText Reseller Silver Partner.
  • N-iX is compliant with PCI DSS, ISO 9001, ISO 27001, and GDPR standards.
  • N-iX partners with Fortune 500 companies helping them make the most of their investment in computer vision development. 
  • N-iX is a reliable computer vision development company that has a proven track record in developing computer vision solutions supported by real-life case studies in various industries.

If you need help with implementing your computer vision solution, contact our experts, and they will be glad to answer your questions.

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