According to SAS, the estimated market for facial recognition for Computer Vision will be $9.6 Billion by the end of this year and CV will be used in almost all modern technologies and gadgets. The applications of Computer Vision is wide and diverse starting from smart cities to medical science, surveillance cameras to smart cars and beyond.
The added traits that Computer Vision brings to the table makes it very convenient for engineers to build applications that cater to various use cases. In the current times, leveraging Computer Vision on Edge devices is drawing major attention for its quick response abilities to provide better & faster outcomes by processing the data locally.
We shall learn more about Computer Vision on Edge but first, let us get familiar with what exactly is Computer Vision.
What is Computer Vision?
Just like Human Vision, Computer vision (CV) is the ability of devices to interpret what they see. Computer Vision is a part of AI where the machine captures videos, images and inferences accordingly. This process is quite complex because it involves multi-dimensional data processing that will help in making real-time decisions based on the trained modules and AI algorithms.
Experiments with Computer Vision started in the 50s when the first neural networks were taught to detect edges of objects into simple categories such as circles and squares. By the time 90s arrived, preloaded images for analysing and facial recognition were readily available. This data helped tremendously for machines to recognise and classify specific people in photos and videos.
Today, the advances we made in Computer Vision is astounding. In less than a decade, the accuracy rate of object detection and classification has jumped from 50 percent to 99 percent. They even surpass humans in detecting and reacting to visual inputs.
Take an example of the breakthrough usage of Computer Vision for COVID case detection. Alibaba’s research branch Damo Academy developed an AI model that was capable of detecting coronavirus in humans by using a chest Computed Tomography (CT) scan and image processing.
Why Computer Vision is being implemented on Edge devices?
Another spectrum of computer vision that has been widely used is Computer Vision coupled with Edge devices. Computer Vision on edge can be utilized for various usage verticals like drones, autonomous cars, assembly lines, robots, etc. enabling them to act on different inputs in real-time and be unfettered from the cloud.
With cloud-based devices, hundreds of devices transmit data simultaneously. This leads to not only latency but also an increase in bandwidth costs and bottlenecks causing a spike in the cost of production. Edge devices overcome these hitches by using local data and storage units. Additionally, edge devices are more robust since they can operate autonomously and have offline capabilities. That is why Computer Vision is being implemented on edge devices because of the reliability it provides.
Computer vision on Edge usage across different fields
The reason why Computer vision on Edge is embedded in modern devices lies in the fact that it can be applied in real-world scenarios to get optimal output for various inputs and problems. CV coupled with Edge devices provides valuable insights and helps in taking real-time actions.
Today, CV is already being used in health devices, security devices, manufacturing units, smart cities, robotics & more. CV has grown as an integral part of AI technology so much so that it finds its applications in complex functions such as the radiology department to detect cancerous cells, body movements, gait and posture.
CV on Edge is gaining momentum in the manufacturing industry, especially in the safety and quality assurance department. Cameras are being equipped with smart devices that run a machine learning model. The model inferences data and returns output that uplifts the production environment by monitoring and checking equipments, worker safety, compliance and other processes.
Future of CV
Computer Vision with its boundary-pushing applications is defining the present and the future. With the continuous breakthrough results in medical science, smart city requirements, drones, cameras, manufacturing assemblies with the help of advanced deep learning techniques, neural networks, machine learning are continuously improving the applications of Computer Visions.
Kbvresearch report says that the Global Computer Vision Market size is expected to reach $17.9 billion by 2026, rising at a market growth of 10.1% CAGR during the forecast period.
ENAP Studio Can Help You Build CV Applications
If you happen to be an AI developer or a company in the AI space, ENAP Studio can help you build CV applications faster and better. ENAP can be utilized for Computer Vision tasks to perform some of the activities like:
- Training an AI model – An AI model can be trained easily using ENAP Studio. Select a model to train, select the default parameters like epochs and batch size for best training performance, upload the dataset and begin training
- Optimizing an AI model – The resulting AI model from the previous step can be optimized for your target hardware easily through ENAP Studio without compromising the acceptable level of accuracy. Our proprietary optimizing techniques improve model performance and accelerate inference.
- Deploy model – Seamlessly deploy the trained and optimized models with a few clicks on all major hardware including NXP, Intel Devices, NVidia Jetson, etc.
- Model Zoo – Consists of pre-built and pre-optimized models for various tasks like hard hat detection, face detection, person detection and vehicle detection which can be used for training.
Want to learn more about the use cases of ENAP Studio? Check out the use cases – https://edgeneural.ai/enap usecases/
