In recent times, the domain of computer vision has made significant progress and has been utilized for various purposes such as surveillance systems, medical imaging, plant disease recognition, facial recognition, and autonomous vehicles. With the increasing requirement for immediate image processing and analysis, a question arises about the technology that will dominate the future of computer vision: will it be embedded devices or edge devices? This article will examine both alternatives and analyze their capabilities and possibilities.
Embedded Computer Vision
Specialized computing systems known as embedded devices or embedded systems are designed to perform specific functions within a larger system. These devices are usually integrated into a larger product or infrastructure, like an industrial machine, drone, or camera. Embedded devices intended for computer vision have dedicated hardware and software components that enable real-time image processing and analysis
Advantages of Embedded Devices
Embedded devices offer a range of advantages, including:
Efficiency: Designed to perform a specific function or set of functions efficiently, reducing the amount of power they consume and the cost of the hardware.
Customizability: Highly customizable, allowing developers to tailor them to specific applications. This flexibility makes it possible to create highly specialized systems that are optimized for a particular task or set of tasks.
Size and weight: They are typically small and lightweight, making them ideal for applications where size and weight are critical considerations.
Harsh environments: Often designed to operate in harsh environments, such as extreme temperatures, humidity, and vibration. This makes them ideal for use in industrial, automotive, and aerospace applications.
Low cost: Because they are designed for a specific function, embedded devices can be optimized for efficiency, reducing the cost of the hardware.
Computer Vision on the Edge
Unlike embedded devices, which are closely connected to the hardware they are embedded in, edge devices are separate units that can be connected to various sensors and cameras. These devices are equipped with powerful processors and sufficient memory to handle computationally intensive computer vision algorithms. Edge devices are a recent development that brings computation power closer to the data source.
Advantages of Edge Devices
Edge devices offer a range of advantages, including:
Real-time data processing: Edge devices are designed to perform data processing and analysis at the edge of a network, rather than in a centralized data center. This enables real-time data processing and analysis, reducing latency and improving the performance of applications that require immediate data processing, such as autonomous vehicles, smart cities, and industrial automation systems.
Improved performance: These devices are typically more powerful than embedded devices and are capable of performing more complex tasks, such as object detection, tracking, and recognition.
Connectivity: Edge devices can be connected to various cameras, enabling them to gather and process data from multiple sources.
Flexibility: Highly customizable, allowing developers to tailor them to specific applications. This flexibility makes it possible to create highly specialized systems that are optimized for a particular task or set of tasks.
Disconnected or low-bandwidth environments: Edge devices are designed to operate in disconnected or low-bandwidth environments, ensuring continued functionality even when there is limited or no connectivity to the central data center.
Conclusion
In conclusion, the choice between embedded devices and edge systems in the context of computer vision depends on the specific requirements of the application. Embedded devices are well-suited for applications where size, weight, and power consumption are critical considerations, while edge systems are better suited for applications where real-time data processing and analysis are critical considerations. Developers and system integrators must carefully evaluate the trade-offs between these technologies to determine which is the best fit for their specific application.