pibot:lesson5
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| pibot:lesson5 [2021/12/02 19:40] – [Algorithm Explanation] jstruebel | pibot:lesson5 [2022/10/20 19:53] (current) – external edit 127.0.0.1 | ||
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| + | ====== Objective ====== | ||
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| + | This lesson will show you how to make the PiBot detect and classify different objects that it can see with its camera. It will show you how to program the PiBot to detect and classify different objects with the camera and display them on a webpage with the video from the camera. It will also show you how to modify the webpage from [[: | ||
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| + | ====== Parts Required ====== | ||
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| + | The parts below are required to complete this lesson. Note that all parts except the heatsinks and objects to be detected are included in the [[https:// | ||
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| + | - Assembled PiBot from [[: | ||
| + | - Raspberry Pi Heatsinks | ||
| + | - Objects that can be detected from the list below | ||
| + | - People | ||
| + | - Cars | ||
| + | - Airplanes | ||
| + | - TV or Monitor | ||
| + | - Chairs | ||
| + | - Laptop | ||
| + | - Dog | ||
| + | - Cat | ||
| + | - Backpack | ||
| + | - Tie | ||
| + | - Skateboard | ||
| + | - Cup | ||
| + | - Fork | ||
| + | - Knife | ||
| + | - Spoon | ||
| + | - Cell Phone | ||
| + | - Clock | ||
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| + | ====== Hardware Assembly ====== | ||
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| + | - Remove the protective backing from the heatsinks and stick them to the top of the chips on the Raspberry Pi\\ \\ <WRAP round tip>The heatsinks help keep the chips from getting too hot when the Raspberry Pi is processing the images from the camera</ | ||
| + | - Turn the PiBot on and place it so that it can drive around\\ \\ <WRAP round tip> | ||
| + | - Place object around in front of the PiBot so that it can see them and drive around | ||
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| + | ====== Software ====== | ||
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| + | Select the appropriate link below for instructions to setup the software on the PiBot and an exploration of how it works. | ||
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| + | * [[: | ||
| + | * [[: | ||
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| + | ====== Algorithm Explanation ====== | ||
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| + | This lesson uses a method or algorithm called a machine learning neural network to process images and detect certain objects within the image. Machine learning algorithms are composed of certain rules which are then used by the computer to learn about the characteristics of the data. The specific algorithm that is used for this lesson learned what characteristics make up the different objects listed above when they are seen in an image. The rules that were used for this algorithm are called a neural network. | ||
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| + | Neural networks are designed to mimic the way that our brains process information draw conclusions. A neural network is composed of several layers of neurons that connect to each other and the layers above and below them. Each neuron takes in data from the layer above it and processes it based on hints that it is given called weights. It then passes the results of its processing to the layer below it. The top layer gets its input directly from the data that is being processed and the bottom layer outputs it data back to the program that is running it. During training of the neural network, the result from the bottom layer are graded and the weights are updated to achieve a higher score. After training is complete, the individual weights of each neuron are saved into a model that can then be used by another program such as in this lesson to detect objects in an image. These final weights represent the neural network' | ||
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| + | Machine learning neural networks can never provide a 100% certain output. They always provide results as probabilities, | ||
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| + | [[: | ||
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