Diving into a computer vision project can be challenging as there are many difficult decisions one needs to take before even getting to the product development stage. You need to find software development engineers who will fit your project needs, have the right experience, understand your business area and how to mitigate potential risks.
With the current state of the labor market, there is a high demand for computer vision developers. It requires a significant investment of money and time to find, hire, train and retain the developers that have the right skill set, so it’s more efficient to find a reliable software development partner instead. How do you partner with a computer vision development company which has relevant experience, can build a dedicated software development team, choose the right tech stack, and deliver a top-of-the-line product?
In this article we will share insights into the process of choosing a trusted computer development partner with relevant expertise. You will learn how to leverage computer vision and artificial intelligence to be at the forefront of digital transformation and overcome key challenges.
We create AI software - and we do it well. Talk to us to get your project started today
Over the past decade, artificial intelligence and computer vision has improved greatly, allowing computers to sense and analyze the world almost like a human. Facial recognition, autonomous vehicles, smart video surveillance and much more have become possible thanks to the rapid development of computer vision technologies.
Companies all over the world have come to adopt computer vision to make their operations more efficient, and this trend is on the rise. The global computer vision market was valued at $10.6B in 2019 and is expected to reach $19,054.9B by 2027. The computer vision market is already growing at a fast pace, and will continue to do so in the near future. The interest in computer vision will continue to rise as well, making this area of business safe to invest in.
There are multiple factors that drive the adoption of computer vision and artificial intelligence, like the rise in demand for vision-guided robotic systems, government incentives, and a rapid increase of interest towards computer vision systems from businesses. Global events, like a recent pandemic, have also increased the demand for artificial intelligence systems.
Computer vision has found applications in many different fields, like smart diagnostics in healthcare, facial recognition for public surveillance, predictive maintenance and quality inspection in manufacturing, digital advertising, smart KYC practices and AI-assisted onboarding, and much more. The core applications of computer vision systems are as follows:
According to Forrester research, over half of all global purchase influencers say that computer vision will become more important for their business in the coming year. Nearly half of all respondents say that their business is already implementing or is interested in implementing computer vision systems in the following year. There are multiple reasons these companies show interest in computer vision implementation:
The increase in interest towards computer vision, a growing number of successful computer vision implementation stories spurs the demand for computer vision developers with relevant experience. However, finding the right inhouse computer vision engineers can prove to be a challenge, which is why businesses - from startups to Fortune 500 companies - choose to outsource their computer vision development needs.
A great increase in demand for computer vision developers has caused a skill gap on the market. Lack of relevant AI skills is one of the major obstacles when it comes to computer vision system development, making highly skilled CV engineers to be in high demand, which drives companies to explore software development outsourcing.
There are multiple steps you can take to make a well-informed decision when it comes to hiring a computer vision development company:
Not every computer vision project will be successful or profitable, so it is crucial to have a clear understanding of your project potential before diving head first into the development process. Choose a computer vision outsourcing vendor who provides AI and ML validation services. This step will ensure you have a clear understanding of the development strategy, have time, money and detection accuracy estimations, as well as assessments on profitability, both short term and long term. This step will also help you get a thorough analysis of the dataset and receive improval recommendations.
We will assess the quality of your dataset and build a test model
Dataset is at the core of any successful computer vision project. The dataset quality has a direct influence on the ML model accuracy, with low quality dataset yielding poor recognition scores. The quality of the dataset is comprised of the total count of images, image quality, image size, balance of image groups, etc. For many computer vision projects, putting together an adequate dataset has proved to be one of the biggest challenges. Here are a few ways to solve this task:
Choosing an effective data storage option is one of the cornerstones of overall system efficiency and data security. When it comes to computer vision development, there is often a choice between a data warehouse and a data lake. Data lakes are usually used in advanced machine learning projects where the data is collected from multiple sources in real-time and is stored in its original format. Broad analysis of data gathered over a long time often calls for a use of a data lake, while data warehouse is a better fit for day-to-day operations. Many companies, however, go for both a data lake and a data warehouse, as it allows them to reap the benefits from both.
Our client evaluates the construction of buildings and prepares bills of quantities. To accurately prepare a bill of quantities and provide a price estimation, a technical drawing needs to be analyzed and a lot of data needs to be extracted. Our client approached Businessware Technologies to develop a computer vision-poweree system which would perform the analysis automatically, extracting relevant features and generating a bill of quantities.
The system performs extensive technical drawing analysis and extracts all relevant data:
Floor plans come with a bill of quantities that contains information about the different labels used in the technical drawing, as well as information about the materials. The PDF tables are not an ideal way to handle large amounts of data since they cannot be edited and the data cannot be sorted or filtered.
We have developed a subsystem that scans the PDF tables and turns them into Excel tables without changing the original structure of the table and keeping the data integrity.
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.
Our client is a local museum striving to augment the visitor experience with modern technologies. Businessware Technologies was approached to develop a computer vision solution for real-time art recognition.
Here at Businessware Technologies, we have developed an app which performed under significant limitations:
As our client didn't have any experience with AI nor did they want to hire in-staff ML developers, we faced a significant limitation: we couldn’t retrain the image recognition model when new art pieces are added to a collection.
Another limitation was how quick the detection and recognition process would have to be. Our client requested the recognition process to take less than 1 second.
The app uses keypoint detection to recognize art pieces in real-time and provide their description. The algorithm works just as well as a machine learning model would for their application, but meets the requirements of the project, which regular ML models couldn’t do.
Our client didn’t have to invest in servers to run the machine learning model from: our app does all of the heavy work on the mobile device. The recognition process on average takes ~600 ms, depending on the type of mobile device used.
If you have a computer vision project in mind and need help with implementation, contact our manager and they will be happy to help you.