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Project14 | Vision Thing: Beaglebone AI Your Vision Thing Project!

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Vision Thing

Enter Your Project for a chance to win an Oscilloscope Grand Prize Package for the Most Creative Vision Thing Project!

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In the Comments Below: Tell Us How You Would Use the BeagleBone AI for Your Vision Thing Project!

 

We'll Send Out Boards for Project Proposals that Use Them! 

 

 

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We are offering up to 20 FREE Beaglebone AI Boards in exchange for Vision Thing projects that use them!

 

Beaglebone AI Cooling Cape Addon Available from :  BB AI cooling Addon board available

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We are offering up to 20 FREE Beaglebone AI Boards in exchange for Vision Thing projects that use them!

 

What is a Vision Thing project and how do you use the BeagleBone AI to do one?

 

There's a lot of variety with how you choose to implement your project.  It's a great opportunity to do something creative that stretches the imagination of what hardware can do.  Your project can be either a vision based project involving anything that is related to Computer Vision and Machine Learning , Camera Vision and AI based projects, Deep Learning, using hardware.  Or, it can be a graphics project involving something graphical such as adding a graphical display to a microcontroller, image processing on a microcontroller, image recognition interface a camera to a microcontroller,  or FPGA - camera interfacing/image processing/graphical display.

 

What makes the Beaglebone AI suitable for Vision Thing  Projects?

 

BeagleBone AI is a high-end board for developers building artificial-intelligence and computer-vision applications. Its main AI features include a Texas Instruments (TI) AM5729 system on chip (SoC), TI C66x digital-signal-processor (DSP) cores and embedded-vision-engine (EVE) cores. The board has the same form-factor as the popular and cheap BeagleBone Black but with much higher specifications.

 

 

The AI-ready board comes with 1GB RAM and 16GB on-board eMMC flash with a high-speed interface, a USB Type-C port for power and a dual-role controller, and a USB Type-A host. There's also Gigabit Ethernet and Wi-Fi. With preinstalled software, the BeagleBone AI also saves buyers from having to download equipment to get the device up and running.

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The Most Creative Vision Thing Wins a Keysight DSOXO11G Oscilloscope!

Learn more by visiting:

So: what do you have to do to be in the running for one of the brand new Beagleboard AI? Just follow the instructions below!

 

1. Register and/or log-in to the element14 Community

2. Leave a comment on this post or on Project14 | Vision Thing: Build Things Using Graphics, AI, Computer Vision, & Beyond!  telling us what you'd like to build for your Vision Thing project with the new Beagleboard AI!

3.  Once you receive a new board submit your finished Vision Thing  project for a chance to win a Keysight DSOXO11G Oscilloscope!

 

 

The Most Promising Vision Thing Project Proposals Win a Free Beaglebone AI to Use In Your Vision Thing Project!

 

Submit Your Completed Project in Vision Thing  for a Chance to Win a Keysight DSOXO11G Oscilloscope!

  • I started around playing with Development Board just 1 year back and am also a new member to element14. I really love the community here I visit element14 and hackster.io community daily without fail. I just really love the quick responses which I get sometimes and also information about new Dev boards. I have been playing around with different dev boards from past 1 year (I know am really late).

    I recently got my very own first development board -> AVNET Azure Sphere MT3620 Starter Kit. (Brought for Rs.318 used a promo code)

    It was total FUN exploring the possibilities one of friends already owned a kit containing many sensors, wires some breadboards. (He never used them for past 2yrs) my luck I got my first board and then my friends kit.

    Another incidence I was just learning about Deep Learning programming with Python. I already knew Python but wasn't good with TensorFlow.

    Maybe I can use the BeagleBoard AI for one of my planned projects -> Smart Cycle Module!
    A single module (Which wont drive the cycle but collect from the cycle) capable enough to collect environment data and energy produced from cycling. Being an Indian Citizen Electric Vehicles and smart cycles cost a lot.  Even a geared cycle cost a lot.
    So what my Module does?
    -> First collect energy
    1) From small solar panels attached to the cycles body.
    2) From Piezoelectric plates attached securely just below the cycle tube.
    3) Dynamo's attached to both wheels.
    -> Collect Data
    1) Use AI to detect pot holes on road, take a picture and send them to a dedicated network of such Smart Cycle Modules. Which would warn other users about the roads with pot holes and a central computer would analyze the collected data and send reports to the public road repair services listed.
    2) Collect environment data to predict weather and road conditions like traffic.
    3) Collect Health data for personal use with other existing available health monitoring apps.
    -> Exchange data and energy for good.
    1) Use IOTA network for data and token exchange.
    2) Exchange collected energy for tokens which can be further used to recharge your other electric vehicles at IOTA charging points.
    3) Exchange data for more data about public transport.
    Being a junior software programmer security is one of my main goals while doing any sort of projects. So am already trying to create a dedicated protocol for these IoT services. Am not sure about the protocol development time.
    Current Status -> Creating some DL Models, learning about BeagleBoard AI Programming, And procrastinating (THINKING).
  • in reply to kk99

    Using ANN for this type of medical activity has been around for many years - I can remember looking into the literature when I was studying many, many years ago. At that time it never really got going as the big question was not the diagnosis but who was responsible. If a mistake was made, who would get sued, and it seemed to be the programmers! They must have sorted out this problem by now.

     

    Dubbie

  • in reply to Fred27

    Sounds interesting. I had a friend who used to record hang-gliding competitions, where they had a GPS on-board and their path was recorded in real-time. I think it used a PIC or possible an MBED.

     

    Dubbie

  • Few years ago I saw attempt to use neural networks for image classification for computer aided diagnosis (CAD) e.g. initial check for the presence of *** cancer on images.

  • A friend is heavily involved in canoe racing. Apparently the timing is a haphazard affair with people pressing stopwatches, writing down the number on the canoe, entering it into Excel, etc. It's fraught with error and completely wrong most of the time.

     

    My proposal is to train the BeagleBone AI to recognise a canoe and read the number. A simple entry into a text file (with a timestamped photo for manual checking) should help a lot.

  • Having more-or-less decided on something for the Vision Thing based on Arduino and possibly Processing as well, I have just seen this opportunity to use the BeagleBone AI. Being mostly used to using Arduino Nanos and the odd MAKERZERO and similar the BeagleBone AI seems to bucket loads of processing and memory and connectivity, it must need it's own power station if they are all used simultaneously! To be honest, I'm not sure that my Vision Thing idea needs all this processing power, just the FPU and large memory, but if I get one I'll see what else I could use on it.

     

    My idea is to make my own 'camera' out of 64 small LDRs arranged into an 8x8 grid array, connecting to some processing unit such as a MKRZERO (as I have one) or maybe a BeagleBone AI (if I get one). Analogue multiplexers would be used to connect to the ADC inputs available to obtain a grey scale image. Each bit of the image would be connected to an input on an Artificial Neural Network (ANN). For a simpler feed forward ANN this would require 64 neurons in the input layer, probably one hidden layer of something like 128 neurons, plus the output layer where the number of neurons is determined by the number of classes or types to be recognised. My initial activity will be to try and train the ANN to recognise the 26 letters of the alphabet. I would also like to implement some sort of graphical display to show the current image being obtained from the LDR camera. Initially I might try to use the LDR camera without a lense with just the letter being placed directly onto the surface of the LDRs, but if I can work out some lense stuff (that's a technical name used by anyone who doesn't know anything about optics) I might even try to make a proper camera out of it and obtain images of the 'world' . Then it would be possible to try and recognise a much wider range of classes or types. I could just use an existing camera, but where would be the fun in that!

     

    The main reason for using something like the BeagelBone AI is that an artificial neuron is essential a sum of products mathematical operation, so for example each neuron on the input layer, would have 64 inputs (one from each LDR) multiplied by an individual weighting constant (both of which work best as floating point values) which are then all added together, so another 64 addition operations. This is then replicated on each of the 64 input neurons (so 64 x 64 multiplies and so on). These values are then passed to the hidden layer where the number increases to 128 x (64x64) multiples, and then again to the output layer of 26 x (128 x (64x64) = 13,631,488 plus all the additions. So over 13 million floating point operations and this is why many ANN systems do not use such large numbers of neurons. Plus you need all the memory to store all the values. If I used a MKRZERO I would probably have to reduce the number of neurons in the hidden layer and possibly the output layer as well so only recognising a handful of letters, but maybe the BeagleBone AI can perform a brute force and hence quicker, solution. I'm not an expert on any of this so I might be completely wrong.

     

    It might all collapse and die at the first hurdle but I'm ready to give it a go.

     

    Dubbie

  • I have a cape from a Beagle Bone Black which contains a Spartan 6 FPGA. I would like to build a camera interface utilizing the FPGA attached to Beagle Bone AI. Then I can simulate the adjustable cruise control and lane keep assist in a car. That way a warning when the car I am driving drifts into another car whether front or either side can be prevented by buzzer alert. The distance would need to be adjustable as well.