Embedded and Single-Board Computer Vision: Running Deep Neural Nets
Embedded and Single-Board Computer Vision: Running Deep Neural Nets

Deep learning (DL) and neural networks are extremely widespread in different computer vision (CV) applications. Indeed, many typical problems (like object recognition or semantic segmentation) are effectively solved by the convolutional neural networks (CNNs). In this article, we are going to discuss how to utilize CNNs on embedded devices. Neural networks today are ubiquitous. In particular, it is hard to imagine computer vision without them.

Embedded and Single-Board Computer Vision: Introduction
Embedded and Single-Board Computer Vision: Introduction

Computer vision (CV) and machine learning (ML) algorithms solve a tremendous amount of problems. However many businesses often do not understand what hardware to choose for running your favorite neural net or some advanced image and video processing pipelines. With this blog post, we start a series of articles about embedded vision and specific practical things you need to know before making your choice.

Binary Marker Recognition on Raspberry
Binary Marker Recognition on Raspberry

Fiducial markers are widely used in various applications like robot navigation, logistics, augmented reality. Fig. 1. Applications of fiducial markers Advantages are obvious High contrast Simple code generation Resistance to extremal angles However, when we deal with a large number of markers, real-time recognition becomes challenging, especially on embedded devices with low power CPUs on-board.