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 exactly how to mitigate potential risks.
The race to find and hire computer vision developers in the global market is hot. It takes time and money to find, hire, and retain the experts you need. So how do you find and 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 on how to choose a trusted computer vision development company that has the right experience, a vast talent pool, and whose reputation is supported by real-life success stories. You will discover how to approach digital transformation with machine learning and computer vision and overcome key challenges.
Why companies invest in computer vision
Modern machines can perceive and analyze the world through visual means. 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 improve efficiency, precision, and control. The global computer vision market is expected to reach $24,37B by 2026. 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 software development services market are the rapid adoption of process automation, the surge in demand for vision-guided robotic systems, and the adoption of other solutions that complement computer vision, such as Data Analytics.
Based on the component, the computer vision market is segmented into hardware and software. The PC-based computer vision systems segment led the market and accounted for more than 52% of the global revenue in 2021.
Computer vision is now applied across all industry verticals. It is used in disease diagnoses in healthcare, security biometrics, quality inspection in manufacturing, obstacle detection and guiding autonomous vehicles in transportation, ad recommendations in digital advertising, etc. The applications of computer vision across industries fall into the following categories:
- Quality Assurance & Inspection
- Positioning & Guidance
- Measurement
- Identification
- Predictive Maintenance
- 3D Visualization & Interactive 3D Modeling
The growing number of use cases and success stories spurs demand for computer vision experts 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.
Read more: Finding the right computer vision engineer: 3 elements of success
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. According to the Bureau of Labor Statistics, the job outlook for computer vision developers will be growing by 21% between 2021 and 2031 as each computer vision software development company tries to hire computer vision developers. Such a drastic increase in demand has caused a skill gap in the market. The same is true for the UK market where 81% of businesses say a talent shortage is the biggest hurdle to AI adoption. Thus, more companies are exploring various outsourcing options, such as nearshoring to Latin America or delegating their projects to European partners.
How to select a computer vision development company?
When selecting a computer vision development company, there are several important factors to consider. It is vital to ensure that the company is knowledgeable about the specific problem domain and has experience in developing computer vision solutions. But that is just the tip of the iceberg, you need to consider a lot more:
- Compile a list of computer vision development companies you will review in terms of their service quality, expertise, and security standards.
- Check the reviews, references, and video testimonials of the vendors you consider as potential partners. You can find this information on their websites, LinkedIn accounts, and ranking platforms;
- Consider the size of the computer vision development companies and their recruitment capabilities, as mid-size and large providers are more likely to have resources you need internally than smaller companies;
- Pay attention to administrative facilities, equipment, and other infrastructure of your potential computer vision development partner;
- Make sure your potential outsourcing provider complies with international security standards such as ISO, PCI, and HIPAA and has an effective security policy in place;
- Ask your potential vendor the right questions to make sure that the provider is reliable.
- Ask a vendor to provide a portfolio of successfully delivered computer vision projects.
- Assess the skills of computer vision developers 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 services, DevOps, and 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
The Discovery Phase is a critical step in the software development process because it allows you to identify and define the scope of the project, analyze the business requirements, and identify potential risks and challenges. You need to select a vendor that can run a thorough Discovery Phase to map out all the aspects of your project. 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 potential profits 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.
2. 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 for solving 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 depend on the type of data. The most popular techniques for data augmentation are SMOTE or SMOTE NC.
- Incremental learning is a method of machine learning which does not require large amounts of data for training models. 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 samples, the model is trained to be able to predict more accurate results.
- 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 robots to create an efficient adaptive control system for themselves based on previous behavior response experiences.
3. Choose the right data storage
When it comes to data, there arises the question of 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, 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 uses the data from a DL that has been cleaned and structured.
4. 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 several 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 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 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, the 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 carefully examining 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 for 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. These features allow for real-time fine creation based on captured data.
Why choose N-iX as your computer vision development partner?
Computer vision is a groundbreaking technology that is likely to revolutionize many industries within the next decade. It is important to consider the advantages of this technology and select a reliable technology partner before devising a custom software solution tailored to your business objectives. If you want to incorporate this technology into your business for increased efficiency, you should seek the help of a reputable partner with expertise in Data Science, AI, Machine Learning, and computer vision. The N-iX team provides just that, offering you our expertise and helping you reach a new level of efficiency.
