Detect objects with bounding boxes
In this tutorial, you'll use machine learning to build a system that can recognize and track multiple objects in your house through a camera - a task known as object detection. Adding sight to your embedded devices can make them see the difference between poachers and elephants, count objects, find your lego bricks, and detect dangerous situations. In this tutorial, you'll learn how to collect images for a well-balanced dataset, how to apply transfer learning to train a neural network and deploy the system to an edge device.
At the end of this tutorial, you'll have a firm understanding of how to do object detection using Edge Impulse.
There is also a video version of this tutorial:
For this tutorial you'll need a supported device. Either:
In this tutorial we'll build a model that can distinguish between two objects on your desk - we've used a lamp and a coffee cup, but feel free to pick two other objects. To make your machine learning model see it's important that you capture a lot of example images of these objects. When training the model these example images are used to let the model distinguish between them.
Capture the following amount of data - make sure you capture a wide variety of angles and zoom level. It's fine if both images are in the same frame. We'll be cropping the images later to be square so make sure the objects are in the frame.
- 30 images of a lamp.
- 30 images of a coffee cup.
You can collect data from the following devices:
Or you can capture your images using another camera, and then upload them by going to Data acquisition and clicking the 'Upload' icon.
With the data collected we need to label this data. Go to Data acquisition, verify that you see your data, then click on the 'Labeling queue' to start labeling.
Collected data, now let's label the data with the labeling queue.
No labeling queue? Go to Dashboard, and under 'Project info > Labeling method' select 'Bounding boxes (object detection)'.
The labeling queue shows you all the unlabeled data in your dataset. Labeling your objects is as easy as dragging a box around the object, and entering a label. To make your life a bit easier we try to automate this process by running an object tracking algorithm in the background. If you have the same object in multiple photos we thus can move the boxes for you and you just need to confirm the new box. After dragging the boxes, click Save labels and repeat this until your whole dataset is labeled.
Labeling multiple objects with the labeling queue. Note the dark borders on both sides of the image, these will be cut off during training, so you don't have to label objects that are located there.
Afterwards you should have a well-balanced dataset listed under Data acquisition in your Edge Impulse project.
Rebalancing your dataset
To validate whether a model works well you want to keep some data (typically 20%) aside, and don't use it to build your model, but only to validate the model. This is called the 'test set'. You can switch between your training and test sets with the two buttons above the 'Data collected' widget. If you've collected data on your development board there might be no data in the testing set yet. You can fix this by going to Dashboard > Perform train/test split.
With the training set in place you can design an impulse. An impulse takes the raw data, adjusts the image size, uses a preprocessing block to manipulate the image, and then uses a learning block to classify new data. Preprocessing blocks always return the same values for the same input (e.g. convert a color image into a grayscale one), while learning blocks learn from past experiences.
For this tutorial we'll use the 'Images' preprocessing block. This block takes in the color image, optionally makes the image grayscale, and then turns the data into a features array. If you want to do more interesting preprocessing steps - like finding faces in a photo before feeding the image into the network -, see the Building custom processing blocks tutorial. Then we'll use a 'Transfer Learning' learning block, which takes all the images in and learns to distinguish between the two ('coffee', 'lamp') classes.
In the studio go to Create impulse, set the image width and image height to
320, the 'resize mode' to
Fit shortest axisand add the 'Images' and 'Object Detection (Images)' blocks. Then click Save impulse.
Designing an impulse
Configuring the processing block
To configure your processing block, click Images in the menu on the left. This will show you the raw data on top of the screen (you can select other files via the drop down menu), and the results of the processing step on the right. You can use the options to switch between 'RGB' and 'Grayscale' mode, but for now leave the color depth on 'RGB' and click Save parameters.
Configuring the processing block.
This will send you to the 'Feature generation' screen. In here you'll:
- Resize all the data.
- Apply the processing block on all this data.
- Create a 3D visualization of your complete dataset.
Click Generate features to start the process.
Afterwards the 'Feature explorer' will load. This is a plot of all the data in your dataset. Because images have a lot of dimensions (here: 320x320x3=307,200 features) we run a process called 'dimensionality reduction' on the dataset before visualizing this. Here the 307,200 features are compressed down to just 3, and then clustered based on similarity. Even though we have little data you can already see the clusters forming (lamp images are all on the left, coffee all on the right), and can click on the dots to see which image belongs to which dot.
The feature explorer visualizing the data in the dataset. Clusters that separate well in the feature explorer will be easier to learn for the machine learning model.
Configuring the transfer learning model
With all data processed it's time to start training a neural network. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. The network that we're training here will take the image data as an input, and try to map this to one of the three classes.
It's very hard to build a good working computer vision model from scratch, as you need a wide variety of input data to make the model generalize well, and training such models can take days on a GPU. To make this easier and faster we are using transfer learning. This lets you piggyback on a well-trained model, only retraining the upper layers of a neural network, leading to much more reliable models that train in a fraction of the time and work with substantially smaller datasets.
To configure the transfer learning model, click Object detection in the menu on the left. Here you can select the base model (the one selected by default will work, but you can change this based on your size requirements), and set the rate at which the network learns.
Leave all settings as-is, and click Start training. After the model is done you'll see accuracy numbers below the training output. You have now trained your model!
A trained model showing the precision score. This is the COCO mean average precision score, which evaluates how well the predicted labels match your earlier labels.
With the model trained let's try it out on some test data. When collecting the data we split the data up between a training and a testing dataset. The model was trained only on the training data, and thus we can use the data in the testing dataset to validate how well the model will work in the real world. This will help us ensure the model has not learned to overfit the training data, which is a common occurrence.
To validate your model, go to Model testing and select Classify all. Here we hit 92.31% precision, which is great for a model with so little data.
To see a classification in detail, click the three dots next to an item, and select Show classification. This brings you to the Live classification screen with much more details on the file (you can also capture new data directly from your development board from here). This screen can help you determine why items were misclassified.
Live classification helps you determine how well your model works, showing the objects detected and the confidence score.
With the impulse designed, trained and verified you can deploy this model back to your device. This makes the model run without an internet connection, minimizes latency, and runs with minimum power consumption. Edge Impulse can package up the complete impulse - including the preprocessing steps, neural network weights, and classification code - in a single C++ library or model file that you can include in your embedded software.
Running the impulse on your Raspberry Pi 4 or Jetson Nano
From the terminal just run
edge-impulse-linux-runner. This will build and download your model, and then run it on your development board. If you're on the same network you can get a view of the camera, and the classification results directly from your dev board. You'll see a line like:
Want to see a feed of the camera and live classification in your browser? Go to http://192.168.1.19:4912
Open this URL in a browser to see your impulse running!
Object detection model running on a Raspberry Pi 4
Running the impulse on your mobile phone
On your mobile phone just click Switch to classification mode at the bottom of your phone screen. Point it at an object and press 'Capture'.
Integrating the model in your own application
Congratulations! You've added object detection to your sensors. Now that you've trained your model you can integrate your impulse in the firmware of your own edge device, see the Edge Impulse for Linux documentation for the Node.js, Python, Go and C++ SDKs that let you do this in a few lines of code and make this model run on any device. Here's an example of sending a text message through Twilio when an object is seen.
Or if you're interested in more, see our tutorials on Continuous motion recognition or Recognize sounds from audio. If you have a great idea for a different project, that's fine too. Edge Impulse lets you capture data from any sensor, build custom processing blocks to extract features, and you have full flexibility in your Machine Learning pipeline with the learning blocks.
We can't wait to see what you'll build! 🚀