Embedded vision is the practice of integrating computer vision into computers to analyze images or videos. In conventional methods, the camera and computer are two separate entities that often take up space and cost more to build. Vision systems often rely on the computer with an interface card to import images from the camera. It also needs to use image analysis software. All these systems can be bulky and complicated to manage.
Recent advancements in technology, however, have allowed cameras and processing boards to become smaller and more powerful. As such, they are easier to integrate into systems without considerable cost increases.
Embedded vision essentially combines embedded or microprocessor-based systems that perform dedicated functionalities inside a network and computer vision devices or those that can analyze images. This combination allows machines to carry out intelligent tasks.
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What Are the Use Cases of Embedded Vision?
The use of embedded vision is increasing, with a host of industries enjoying several advantages. The systems’ compact size, cost-effectiveness, and energy-efficiency are just some of these. Given that, embedded vision systems are used for applications such as:
Embedded vision systems are used in developing robotic arms. The technology lets developers improve the capabilities of robots so these can see and react to their environment. As such, more flexible and productive machines are created.
Embedded vision allows robots to see and analyze what items to pick and where to place them in assembly lines, making the process more organized. It also lets robots carry out the necessary quality checks and inspections.
These processes can help the manufacturing industry save tons of resources involved in manual labor, repairs, and testing.
Embedded vision is also proving beneficial in the healthcare industry, specifically cancer detection, particularly for skin cancer. One mobile app funded by IBM uses the technology to detect skin cancer signs in moles. Users can take pictures of their moles and send these to the system. The machine then does a real-time analysis of the mole’s condition. The app uses an algorithm that can determine if a mole is cancerous or not. The app also searches for dermatological specialist recommendations for added convenience so users can start immediate treatment if needed.
In an era where technology plays a crucial role in the transportation industry, the introduction of embedded vision technology can help improve self-driving automobiles’ capabilities. It can incorporate gesture and facial recognition systems into cars to easily detect barricades and other road hazards.
A self-driving car, for instance, can have embedded vision sensors to check the driver’s eye and head movements for signs of sleepiness while driving. It can also automatically control other functionalities such as the stereo volume.
In some places like airports, business establishments, schools, and retail stores, embedded vision can identify faces in real-time. With an artificial intelligence (AI)-powered device, authorized personnel can easily recognize faces to see if the people coming in and out are suspicious.
In an industrial setting, employees will never have to swipe their security cards to unlock a door. A camera endowed with embedded vision can immediately verify the faces of authorized individuals and automatically grant access. In school, the technology can check if the people picking up the kids are parents or authorized guardians.
Embedded vision systems may not entirely replace computer-based ones, but their applications are already making a massive impact across industries.