Tech Focus: Image Processing at the Edge
In today’s fast-paced, data-driven world, the ability to process vast amounts of visual information quickly and efficiently is crucial for industries ranging from manufacturing to healthcare. Other forms of image processing techniques involve sending data to a centralized cloud or data center, where it is analyzed and returned to the source. However, as the need for real-time decision-making grows, so does the demand for processing data closer to where it’s generated – a process known as edge computing.
In this Tech Focus, we will explore what edge computing means in the context of image processing and vision systems, its advantages, and how industries can benefit from it. We’ll also dive into the hardware that supports edge-based image processing which are integral to machine vision and computer vision applications.
What is edge processing?
Edge processing refers to performing data processing tasks closer to the source of the data, typically at the “edge” of the network. Instead of sending raw image or video data to the cloud for processing, data is analyzed on a local device, such as a camera or embedded vision system, reducing latency and bandwidth usage. This is particularly advantageous in real-time applications that demand immediate action or feedback.
In image processing, edge computing enables the system to make decisions in milliseconds, which is critical for applications such as autonomous vehicles, industrial automation, and surveillance systems. By processing images at the edge, data doesn’t need to be transmitted to a remote server, reducing delay, enhancing security, and minimizing the risk of data corruption during transmission.
Applications that benefit from edge image processing
The shift toward edge-based vision systems offers numerous benefits across a wide array of industries. We’ve taken a look at some key applications that greatly benefit from edge processing in image processing.
Autonomous vehicles, UAV and ROV applications
Self-driving cars, ROVs, robots and drones rely heavily on real-time image processing to detect and classify objects, interpret traffic situations and complex environments. These systems must make split-second decisions to ensure safety and enable immediate reactions as required. Edge computing allows processing of visual data locally, with considerably lower latency than cloud-based solutions.
Smart factories (Industry 4.0)
In industrial automation, the use of machine vision systems for quality control and production monitoring is growing rapidly. Edge-based vision systems can inspect products in real-time, identify defects, and make adjustments without human intervention. This not only improves efficiency but also enhances product quality while minimizing downtime.
Surveillance and security
In the realm of security, surveillance cameras with edge-based processing are crucial. These systems can detect suspicious behavior, identify faces, and trigger alerts instantly. Traffic management systems also benefit from smart cameras, facilitating better traffic flow, reduced congestion and incident analysis. Similarly, surveillance cameras with edge processing features can keep our streets safer, triggering alerts for dangerous or anti-social activities. By processing data locally, edge systems improve response times and ensure privacy by keeping sensitive video data within the local network, rather than transmitting it to the cloud.
Healthcare
In healthcare, medical imaging requires fast and accurate processing to help doctors make timely diagnoses. With edge processing, medical computers and portable diagnostic devices can analyze X-rays, MRIs, and ultrasound images instantly, reducing the wait time for patients and improving the overall efficiency of medical care. The implementation of AI has further expanded the capabilities of medical imaging, predictive diagnostics and patient care planning.
Selecting equipment for edge image processing
Achieving efficient image processing at the edge requires the right combination of hardware and software. Below, we discuss some key hardware components that enable efficient edge-based vision systems.
Embedded vision systems
Embedded vision systems are purpose-built, compact systems designed to perform complex image processing tasks locally. These systems integrate an image sensor and processing unit into a single platform, often powered by efficient ARM-based processors or FPGAs (Field Programmable Gate Arrays).
An embedded vision system can handle tasks like object detection, facial recognition, and real-time analytics without requiring an external server. These systems are ideal for applications where space is limited, such as robotics, smart cameras, and drones.
Embedded systems are highly efficient, offering low power consumption, which is essential for portable devices and IoT applications. They are also scalable, allowing for future upgrades and improvements as machine learning models and algorithms evolve.
Active Silicon has decades of experience in designing, prototyping and manufacturing embedded systems for very specific applications. Our team of hardware, firmware and software engineers work closely with customers in surveillance, medical imaging and factory automation to build customized vision systems perfectly suited to the task at hand. The latest addition to our embedded portfolio, the Oncilla Machine Vision Computer – CoaXPress, has been designed for machine vision applications requiring high processing power using the real-time imaging reliability of the CoaXPress standard.
Frame grabbers
A frame grabber is a valuable component in machine vision systems, responsible for capturing images from cameras and transferring them to a processing unit for analysis. Used in systems that rely on high-speed or high-resolution cameras, frame grabbers ensure that the captured images maintain their integrity before being processed.
FireBird frame grabbers from Active Silicon enable advanced processing capabilities. Our CoaXPress and Camera Link acquisition cards support GPU processing, freeing up space on the system CPU for other tasks. This helps to reduce system latency and improve overall efficiency, making them ideal for applications like high-speed inspection and scientific applications. Additionally, all our high-speed frame grabbers are fully GenICam compliant, ensuring simple integration with existing systems.
Smart cameras
AF-Zoom cameras are compact, modular cameras often used in security, surveillance, and automated systems. They provide outstanding image quality and can be integrated into embedded systems for edge-based processing. These block cameras have excellent flexibility in zoom options, lens configurations and ability to operate in low-light environments, making them suitable for diverse vision system applications. Our range of compact and lightweight Harrier AF-Zoom cameras include zoom options from 10x to 55x, resolutions up to 4K, global shutter models and all provide a variety of advanced camera features.
Active Silicon’s small Harrier Camera Interface Boards mount closely to a block camera and add onboard processing capabilities for edge-based image processing tasks. These boards take data from the proprietary block camera interface and convert into industry standard formats such as 3G-SDI, USB 3.0, HDMI, Ethernet IP and MIPI CSI-2 and allow text and graphical overlays.
With the ability to run alongside AI models for object recognition, motion detection, and image analysis, these cameras can perform complex tasks locally, reducing the reliance on external computing resources.
High-performance computers
To conduct efficient processing at the edge, computers must be powerful enough to rapidly deal with high volumes of data. For many applications it is an additional requirement that they are rugged enough to withstand the extremes of temperature of a roadside application, the shock and vibration of a factory setting or protect against dust and water ingress in a field application. Alongside our Oncilla Machine Vision Computer, we partner with Steatite to offer a range of industrial computers, panel PCs and AI modules which can be certified to rail, military and medical standards as required.
The Future of Edge-Based Vision Systems
Edge computing in image processing represents a significant shift in how visual data is processed and utilized. As industries continue to embrace machine vision and computer vision, the demand for real-time, efficient processing will only grow. Edge computing offers the ideal solution by bringing processing power closer to the data source. This reduces latency, enhances privacy and security, and minimizes bandwidth consumption.
From autonomous systems to smart factories, surveillance systems, and healthcare devices, edge-based image processing is transforming the way we interact with visual data. By leveraging advanced hardware like embedded vision systems, frame grabbers, and block cameras, engineers can build powerful, responsive vision systems that operate in real-time, paving the way for a more connected and intelligent world.
