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  • Shopping Mall Analytics

    Make impactful decisions based on knowing which stores drive traffic, visitor flow, and marketing campaign effectiveness.

  • TECHNOLOGY INNOVATION

    FLIR’s constant innovation in sensor hardware and software results in industry leading accuracy.

  • Best Practices: Training a Deep Learning Neural Network

    If developers need to run deep learning inference on a system with highly limited resources, they can optimize the trained neural network accordingly and eliminate the need for a host system. Much smaller devices like the upcoming FLIR® Firefly® camera can run inference based on your deployed neural network on its integrated Movidius™ Myriad™ 2 processing unit. This article describes how to develop a dataset for classifying and sorting images into categories, which is the best starting point for users new to deep learning.

  • Comparing VPUs, GPUs, and FPGAs for Deep Learning Inference

    A key decision when getting started with deep learning for machine vision is what type of hardware will be used to perform inference. Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Vision Processing Units (VPUs) each have advantages and limitations which can influence your system design.

  • Complete off the shelf 3D system

    High Definition Imaging (HDI) 3D Scanner which produces a digital 3D scan from physical objects in less than two seconds.

  • SIM and iSIM

    One of the goals of biological microscopy is to observe and analyze biological processes and structures on the subcellular scale. However, the size of the smallest structures that can be observed is set by the diffraction limit of light, meaning no detail can be resolved smaller than around 250 nm.

  • Edge Computing

    Using cloud-based image processing can increase latency and network traffic. It can also pose privacy and security risks.

  • Embedded Systems for Machine Vision

    Embedded systems are computers designed for integration with larger pieces of equipment. The computers built into cars, medical instrumentation, and consumer devices like smart TVs are all examples of embedded systems.

  • Exmor R / STARVIS

    The Sony Exmor R and STARVIS families of rolling shutter, global reset sensors provide excellent low-light imaging for visible and NIR light. For applications including microscopy, metrology, and laparoscopy, where a global shutter sensor is not required, the small pixels of Exmor R sensors enable high-resolution sensors in smaller optical formats.

  • HNu Photonics Studies the Effects of Spaceflight on Human Physiology using Blackfly Machine Vision Cameras

    HNu Photonics is a developer of cutting-edge flight and ground based hardware for aerospace and defense sectors. Using Teledyne FLIR machine vision cameras, the SCORPIO-V division of HNu Photonics is working with NASA to better understand how spaceflight and microgravity impacts humans on a cellular level.

  • Comparing Deep Learning Cameras with Smart Cameras

    The Firefly® DL camera provides an easy path to deploying trained networks in the field.

  • Instrumental’s Novel AI Inspection Platform Leverages Teledyne FLIR Machine Vision Cameras

    Manufacturing accounts for more than half of the world’s total gross world product (GWP) at $40 trillion, but 20% of every dollar spent goes to waste, according to manufacturing optimization company Instrumental. This represents a problem worth $8 trillion, or 10% of the GWP. Founded in 2015 by former Apple engineers, Instrumental has developed an optimization and inspection platform combining cloud software, machine vision inspection, artificial intelligence (AI), and electronic test data that aims to reduce waste by enabling engineers to not only stop problems at the end of the inspection line but to fix them upstream as well.