跳至主要内容

How does Google Coral help to build a Smart City?

To reach the true potential of a “smart city,” AI processing will have to move from the cloud onto secure local devices, maintaining individual privacy, reducing data transfer rates, and enabling quicker reaction times for critical systems.
OCCUPANCY DETECTION
With Coral, building managers can detect human presence in a privacy preserving way in order to optimize lighting, climate, and energy utilization, drastically reducing their cost of operations and carbon footprint.
PEDESTRIAN SAFETY
Coral can utilize cameras and image classification techniques to assist people at intersections, and help drivers become more aware of their surroundings. Coral on public buses and vehicles can also monitor for pedestrians and bicyclists when turning or opening doors.
TRAFFIC FLOW
Coral enables sensors on the road and at critical gateways that can help optimize city-wide traffic flow, accommodating for peak traffic hours, special events, weather conditions, and accidents.
POPULATION FLOW
A distributed network of Coral enabled sensors can help city planners understand population flow in physical spaces such as parks, streets, workplaces, or retail centers in a privacy-preserving manner. Feeding that data into a larger model can help distribute shared resources efficiently and improve incident response.
AIR QUALITY MONITORING
Using Coral, governments and companies can build fast and efficient solutions that identify air contamination problems and create preventive measures to limit emissions.
Google Coral devices for Edge AI have released to the market, if you’re interested, you can purchase online. Google has lots of Coral distributors, Mouser, Gravitylink, etc.
for your convenience, I‘ll put the link: https://store.gravitylink.com/global

评论

发表评论

此博客中的热门博文

How to Retrain an object detection model

This tutorial shows you how to retrain an object detection model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator. Specifically, this tutorial shows you how to retrain a MobileNet V1 SSD model (originally trained to detect 90 objects from the COCO dataset) so that it detects two pets: Abyssinian cats and American Bulldogs (from the Oxford-IIIT Pets Dataset). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. The steps below show you how to perform transfer-learning using either last-layers-only or full-model retraining. Most of the steps are the same; just keep an eye out for the different commands depending on the technique you desire. Note: These instructions do not require deep experience with TensorFlow o...
发表评论
阅读全文

How to retrain an image classification model?

Got a tutorial from Google Coral Team: This tutorial shows you how to retrain an image classification model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator. Specifically, this tutorial shows you how to retrain a  quantized  MobileNet V1 model to recognize different types of flowers (adopted from TensorFlow's docs). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. Tip:  If you want a shortcut to train an image classification model, try Cloud AutoML Vision. It's a web-based tool that allows you to train a model with your own images, optimize it, and then export it for the Edge TPU. Set up the Docker container Prepare your dataset Retrain your classification model Compile the model for the Edge TPU Run...
发表评论
阅读全文

Introducing Google Coral Edge TPU Device--Mini PCIe Accelerator

Mini PCIe Accelerator A PCIe device that enables easy integration of the Edge TPU into existing systems. Supported host OS: Debian Linux Half-size Mini PCIe form factor Supported Framework: TensorFlow Lite Works with AutoML Vision Edge https://store.gravitylink.com/global/product/miniPcIe The Coral Mini PCIe Accelerator is a PCIe module that brings the Edge TPU coprocessor to existing systems and products. The Mini PCIe Accelerator is a half-size Mini PCIe card designed to fit in any standard Mini PCIe slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways. https://store.gravitylink.com/global/product/miniPcIe Features Google Edge TPU ML accelerator Standard Half-Mini PCIe card Supports Debian Linux and other variants on host CPU About Edge TPU  The Edge TPU is a small ASIC designed by Google that provi...
发表评论
阅读全文