AIfES - color detection demo

Arduino Nano 33 BLE Sense Rev2 with headers

AIfES

<h3 >Introdution</h3>This is the official project page of the AIfES developers. You want to know what exciting things you can do with AIfES? We will give you some examples through this account.In this example we show how the <a href="https://www.arduino.cc/en/Guide/NANO33BLESense">Arduino Nano 33 BLE Sense</a> using AIfES can learn the colors of three objects to classify them later. We use the colorimeter and proximity sensor of the Arduino Nano 33 BLE Sense to detect the RGB data of an object. The example was inspired by this Arduino <a href="https://blog.arduino.cc/2019/11/07/fruit-identification-using-arduino-and-tensorflow/">project</a>.The AIfES implementation does not require a PC. The data recording, the creation of the artificial neural network, the training and the classification are done directly on the Arduino Nano 33 BLE Sense. Everything happens fully automatically. This example is already integrated in the <a href="https://github.com/Fraunhofer-IMS/AIfES_for_Arduino">AIfES for Arduino library</a>.For more information about AIfES, visit <a href="http://www.aifes.ai/">www.aifes.ai</a>. <h3 >How to start</h3>For this example, you need two libraries which you can download from the Arduino library manager:<ul ><li >AIfES (aifes, the second letter is a capital &#34;i&#34;)</li></ul><ul ><li >Arduino_APDS9960</li></ul>How to install a library is explained <a href="https://www.arduino.cc/en/guide/libraries">here</a>.The example shown here is already integrated in the AIfES for Arduino library. So you only need to open the example sketch. This is how you can find the example in your Arduino IDE:<code >File</code> -&gt; <code >Examples</code> -&gt; <code >AIfES for Arduino</code> -&gt; <code >1_Nano_BLE_Sense</code> -&gt; <code >0_Color_detection</code>As you can see in the video we use different colors on the screen to train the system. We did this to make it easier for you to see the output. You can of course use any object. Black and white objects don't work so well.With this example you can train three different objects. Of course, you can also change the number of objects if you change the code.<h3 >Artificial neural network</h3>The artificial neural network is a fully connected feedforward neural network with the following structure:<figure><img src=https://projects.arduinocontent.cc/95f6238f-af60-45e4-9cb1-7c813492f4b7.JPG /><figcaption>Structure of the artificial neural network</figcaption></figure>Activation functions:<ul ><li >Hidden Layer:\tsigmoid</li></ul><ul ><li >Output Layer:\tsoftmax</li></ul>The weights are randomly generated in a value range from -2 to 2.The ADAM optimizer is used for training, with full batch over 300 epochs.Training data:Five samples per color are taken. <h3 >Video</h3>Here the video where you can see AIfES in action:<div class="se-component se-video-container __se__float-none" contenteditable="false" style="width: 100%;"><figure style="width: 100%; height: 56.25%; padding-bottom: 56.25%; margin: 0px;"><iframe frameborder="0" allowfullscreen="" src="https://www.youtube.com/embed/BGAXLsUgPqY" data-proportion="true" data-percentage="100%,56.25%" data-size="100%,56.25%" data-align="none" style="width: 100%; height: 100%;" data-index="0" data-file-size="0" data-origin="100%,56.25%"></iframe><figcaption contenteditable="true"><div>AIfES - color recognition example on the Arduino Nano 33 BLE Sense</div></figcaption></figure></div><h3 >Images</h3>Here are the colors from the video: <figure><img src=https://projects.arduinocontent.cc/4f689f10-a0d0-4c04-b2a0-344a97aa990c.JPG /><figcaption>The colors from the video</figcaption></figure>

AIfES - color detection demo

Learns the colors of three different objects and can later classify them.