Skip to main content

High-Speed Solutions for Optical Sorting Machines

Modular Solutions for Optical Sorting Machines

Scalable Vision Architecture for Optical Sorting Machines

Modern optical sorting machines depend on advanced vision architectures to deliver high accuracy and consistent performance. As production lines become faster and more complex, vision systems must support higher resolutions and tighter timing constraints. Consequently, fixed-function designs often struggle to scale or adapt over time.

Modular Infrastructure for Optical Sorting Machines

Gidel provides a flexible vision infrastructure that allows system designers to build, enhance, or upgrade optical sorting machines using modular building blocks. These architectures rely on high-performance Frame Grabbers, which provide deterministic image acquisition and stable real-time processing.

As a result, system performance can scale over time without requiring a complete redesign, while preserving predictable behavior and long-term flexibility.

Deterministic Vision Processing for High-Speed Sorting

In high-speed sorting machines, latency and synchronization are critical. Vision systems must process image data in real time while maintaining deterministic behavior. Otherwise, even small timing variations can reduce sorting accuracy.

Deterministic Processing at High Throughput

Gidel’s FPGA-based vision architecture is designed for predictable, real-time image processing. Moreover, hardware-based Compression IPs reduce data bandwidth while preserving the image fidelity required for accurate decisions:

As a result, high-speed sorting machines can scale throughput while maintaining deterministic, low-latency operation.

Compact Architectures for Embedded Sorting Systems

For space- and power-constrained environments, Gidel also supports compact sorting platforms using Mini Jetson Frame Grabbers. These systems combine deterministic FPGA-based vision processing with NVIDIA Jetson Modules, allowing sorting machines to balance throughput, accuracy, and system footprint.

Optical Inspection Architecture for Sorting Machines

Many systems combine inspection and sorting within a single platform. An optical inspection machine typically performs image analysis, defect detection, or classification before triggering sorting actions. However, inspection workloads can interfere with real-time behavior if they are not carefully managed.

Custom FPGA Algorithm Development

To address this, Gidel enables a unified vision architecture where inspection and sorting coexist efficiently. System designers can design, develop, and deploy custom imaging and vision algorithms directly on the FPGA using the ProcVision Suite. This approach provides full control over latency, precision, and processing flow.

As a result, optical inspection machines can implement proprietary algorithms, optimize performance for specific materials or products, and maintain deterministic behavior even in demanding, high-speed environments.

Learn more about Gidel’s real-world advantages in High-Speed AOI Machines: Enabling High-Speed AOI Machines | Gidel

Need more information?




    Resources

    Reuven Weintraub – Gidel Founder and CTO describes how to boost performance in Machine Vision and Imaging systems. This talk has been given at the Vision Show 2024 Stuttgart, during the Vision Days Forum.

    Image processing acceleration on NVIDIA Jetson embedded computer at the Embedded World 2024 show.

    Gidel edge computers and frame grabbers at the Embedded World show 2024.

    A short video introduction to FantoVision edge computers – 2023.

    Boost your vision! Gidel’s high-performance GigE Vision, Camera Link and CoaXPress frame grabbers help overcome bandwidth bottlenecks in high-resolution and/or high-speed computer vision applications.

    Gidel presented its FPGA-based architecture capable of processing Giga+ Pixels per second while maintaining exceptionally low power consumption.
    The session demonstrated how Gidel’s scalable FPGA solutions deliver real-time imaging performance, energy efficiency, and deterministic throughput for advanced vision and imaging systems.

    Reuven Weintraub, Founder and CTO of Gidel, presented Gidel’s solutions to perform high-bandwidth imaging on the edge at InVision TechTalk webinar held on September 2023.

    Gidel founder and CTO Reuven Weintraub present the new with FantoVision edge computers for 10GigE Vision, Camera Link and CoaXPress interface for embedded vision on the edge at the Embedded World Show 2022 in Nuremberg, Germany.

    Gidel founder and CTO Reuven Weintraub explain the benefits of Gidel’s Altera-based high-performance FPGA modules for fast and easy development.

    Gidel founder and CTO Reuven Weintraub present the performance and versatility of Gidel’s GigE Vision, Camera Link and CoaXPress frame grabbers for high-speed image acquisition and pre-processing at the Embedded World 2022 in Nuremberg, Germany.

    Presented by Reuven Weintraub, this talk highlighted Gidel’s expertise in real-time processing over Gigapixel/s image streams, demonstrating how FPGA-based architectures enable deterministic latency, scalable throughput, and efficient handling of ultra–high-resolution vision data.

    Ron Muller presenting Gidel’s first heterogeneous Edge Computer Vision combining FPGA and NVIDIA Jetson at Industrial Vision Days conference at VISION 2021 show in Stuttgart, Germany.

    Need More Information?

    Our team of experts is ready to help you find the perfect solution for your application needs.

    • Free technical consultation
    • Custom solution design
    • Quick response time
    • Expert engineering support

    Or contact us directly:

    info@gidel.com




      Quote
      Gidel
      Privacy Overview

      This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.