Vision Systems in an AI Machine: A Step-by-Step Guide

Vision Systems in an AI Machine: A Step-by-Step Guide

When I was developing the vision systems in my AI machine, there were several steps that I had to take in order to make the vision system work properly. The first step was to install the camera onto the robot itself, ensuring that it would be able to see where it was going and where obstacles might be on the road ahead of it. The second step was to develop code that would allow my computer to recognize any obstacle in its way and stop if need be so as not to hit whatever was in front of it.

Why We Need Visual Perception

The world around us is full of a vast array of visual stimuli, and if we are to survive, we need to quickly recognize and react to different elements that signal changes in our environment. We have millions of years of natural selection working for us.

For example, imagine you’re walking down a forest path when all of a sudden you see a lion charging at you; your survival depends on how quickly your brain perceives something is wrong and allows you to take evasive action. Or consider a more modern example—a car swerving into your lane. If you can identify a potential hazard before it becomes real, then your chances of avoiding it increase dramatically. In both cases, vision plays a critical role in helping us understand what’s happening around us and giving us time to respond appropriately.

What Are Conventional Approaches?

Many vision systems are based on conventional approaches like artificial neural networks, K-means clustering, and decision trees. Although these approaches have demonstrated success in a variety of settings, some of them may not be appropriate for your particular application.

With just a few basic facts about what you hope to accomplish and how you plan to approach it, I can select a vision system that’s right for your specific application. For example, a large manufacturing company is hoping to design a new series of computer chips. They already have a good idea of what they want their final product to look like; they simply need help determining whether or not there will be any unexpected problems along the way. For situations like these, we could use artificial neural networks to construct models capable of detecting and diagnosing potential problems before they become real issues.

How Does Google Approach This Issue?

Most deep learning models require a large number of parameters and computations, so most of our recent efforts have focused on accelerating model training with graphics processing units (GPUs), field programmable gate arrays (FPGAs), and specialized processors, such as Tensor Processing Units (TPUs).

Google Cloud Platform offers several managed services for developing and deploying machine learning applications, including Google Compute Engine for general purpose computing; AutoML to simplify ML workflow; TensorFlow Serving to deploy pre-trained models at scale, and Cloud Machine Learning Engine to build custom ML pipelines. For more information about how we approach these issues, see our whitepaper.

How It Works

Vision systems—algorithms that turn a digital image into something a computer can understand—are one of several components that go into creating artificial intelligence (AI). These systems, for example, are used to help self-driving cars avoid pedestrians on busy streets or monitor supply levels at manufacturing plants.

In order to build a system that takes advantage of machine learning and other advances in computer vision, engineers will first have to familiarize themselves with software such as TensorFlow and Torch7. They’ll also need to learn how convolutional neural networks work, a type of deep learning architecture inspired by biological processes like human sight. Once they’ve built their own system from scratch, developers will be able to train it using a large dataset of labeled images and use it to solve problems like facial recognition or object detection.

Demo Application Using TensorFlow APIs

This tutorial will cover how to build a complete demo application with TensorFlow APIs. To get started, you’ll build a convolutional neural network (CNN) to identify objects in images and video streams. Then, you’ll implement functionality to detect object locations. Next, you’ll use pre-trained models for the detection and classification of popular object categories such as cars, people, and dogs using MobileNets and Cloud Vision API.

Finally, you’ll add a model zoo to your app so that users can select different types of models to run on their input data. By completing all these steps, you’ll gain a better understanding of what it takes to create a full-featured vision system with TensorFlow APIs.

Where Can I Learn More?

For more detailed information about how to implement and use vision systems, consider purchasing The Complete Book of LIDAR for Robotics by Richard A. Wallace. Not only does it include a lot of great techniques for utilizing these systems, but it also offers some insight into machine learning, neural networks, and image processing that may be helpful as you begin implementing your own vision system.

It can be purchased at Amazon here. What’s Next?: Now that you have a basic understanding of what a vision system is and how it works, it’s time to learn how to build one! A step-by-step guide on doing just that will be coming soon. If you would like a head start on working with visual data before then, check out our free tutorial series where we show you how to take visual data from Microsoft’s Project Oxford API and turn them into 3D models using Blender!

Leave a Reply