One of the booms of emerging technologies is Computer vision which aims to replicate human perception and associated brain functions to acquire, analyse, process, understand and thereafter work on an image. Replicating this process is extremely challenging as designers find it hard to analyse what hardware and software is required to perform the exact match to a customer’s requirements and has the maximum probability of selection. The simple looking concept of isolating an image for identification has taken years of research and development to accomplish. After years of hard-work, businesses using computer vision hardware and software algorithms deploying deep learning technologies are witnessing success in identifying objects
As per reports from Tractica, a market intelligence firm focusing on human interaction with technology, the market revenue from actual and predicted computer vision hardware and software is all set to witness new heights in 2022, the biggest surge will be seen in the automotive sector at $15.79 billion closely followed by the consumer sector at $14.1 billion, followed by robotics & machine vision $9.8 billion, security sector $3.79 billion, medical industry at $2.3 billion, sports & entertainment sector at $2.0 billion. The lowest growth will be into the retail sector at $655 million and agriculture sector at $197 million. Computer vision will see tremendous investment by software companies, semiconductor and component manufacturers and product developers all running a race to develop computer vision products supporting major markets.
Inside Computer Vision Systems
Business enterprises are developing computer vision systems embedded into deep learning systems hosted on the edge of the Internet of Things (IoT), in on-board systems, performing inference analysis in the cloud.
The deep learning algorithms that power computer vision systems perform billions of computations for quick results. These systems can employ solutions using GPU, CPU/DSP FPGA, ASIC technologies or embedded FPGA each having its own advantages and disadvantages. The technology of choice remains the GPUs in spite of consuming lots of power and they might be unsuitable for on-board systems. GPUs even do not allow flexibility in hardware configurations customised to specific algorithms.
Of late FPGA and embedded FPGA technology have started to make inroads into computer vision processing. With changing market requirements, implementations using FPGA technologies requite migrating to ASIC technologies. Technology teams need a quick way to ascertain how these algorithms perform on any of these technologies before production, without making a change in their algorithm description.
The Rise of Computer Vision
As the race to achieve 100% accuracy is underway, hardware and software improvements continue to be the drivers of the computer vision market. Convolutional neural networks (CNNs) have been the technology of choice at classification and image recognition and have experienced a steady stream of advances for future gains. The technical advances contributing to this acceleration include:
- Wireless networks being made available to millions of people, with daily expansions.
- The availability of high-bandwidth for image transmits for the purpose of processing and analysis.
- Availability of data storage and access for CNN training with gigantic image databases collected for training networks.
Business Enterprises keen to invent new solutions must overcome these key impediments:
- Barriers to entry as most computer vision advances occur in university and company research labs armed with funds and skilled resources.
- Technical manpower shortage of skilled engineers with computer vision and deep learning knowledge.
- Developments in new algorithms and training solutions which are changing and evolving at a faster pace, making hardware implementations very difficult to complete before the next, superior idea knocks the door.
The Future Opportunities
Computer vision product development offers huge opportunities for design automation, from the design of self-contained automated systems including image analysis, to chip development for servers in the cloud performing deep learning. FPGA, embedded FPGA design flows and custom ASIC are well-established, witnessing a new audience of deep learning developers and computer vision technologies.
Technology has enabled high-level synthesis solutions to enable software algorithm developers having the power to make informed decisions and to select the best-suited hardware technologies for their application without being hardware experts.
The overall computer vision market is currently expected to be valued at $11.94 billion and is growing to likely reach $17.38 billion by 2023 growing at a CAGR of 7.80% from 2018 and 2023. This growth is attributed to many factors among them being the advanced manufacturing 2.0 practices, increased adoption of Industry 4.0 in US and Europe, and the increasing demand for automation technologies arising from emerging countries like as China and India.
Computer vision will see a rise in the coming years from the growth in the non-industrial verticals including the consumer drones, autonomous and semi-autonomous vehicles, wearable devices, and sports and entertainment contributing to the growth of the computer vision market in the years to come.