Studio diip presents: Computer vision demo
Computer Vision demo
Many projects at Studio diip have to do with using deep learning to train a neural network to detect specific objects. Often though, they are built specifically for our customers and we are not able to show the possibilities to the general public. That’s why we decided to spend some time to create an online demo of object recognition. You can use it to test your own image with some standard algorithms that are trained for different kinds of objects. The algorithms are trained on thousands of photos with different themes and you can test them separately on their own pages: Detecting animals, Detecting electronics, Detecting vehicles and Maritime image recognition. The Computer vision demo detects the specific objects in an image and shows where it is located and with how much confidence.
Tug boats in a harbor detected with the Computer vision demo for ships.
Training deep learning networks
For the image detection we use software like: Keras and TensorFlow. Keras is an open source library that is written in Python and works well with the software of TensorFlow. The TensorFlow library is suitable for the development and training of programs that make use of deep learning techniques. With the use of this software we can see how well the algorithm works and optimize accordingly. For the demonstration, the objects are trained on the extensive COCO dataset (Common Objects in COntext). For the examples we have used an algorithm that is trained on a thousand photos with different themes all coming from this dataset. This database contains images of the themed objects that are pointed out by humans and show where the object is hiding in the photo. Therefore the learning algorithm can train itself to automatically recognize what the object looks like in other photos It is also possible to use this technique to detect other kinds of objects.
You can test your own image below, to see how the demo works!
Image recognition with artificial intelligence (AI)
The technology used in the Computer vision demo can be applied in various ways. For example: We can train a network that analyses electronics on an airport or whenever you want to screen for safety issues on big events. The tool can also be helpful in logistics, science, entertainment or on industrial sites. A beginner can use the tool for educational purposes and an advanced scientist or engineer can use it to gather valuable information to automate complex processes. We can use different online and offline images to detect, count or follow objects. Think about security images, satellite images, heath images or even online streaming. We can embed networks like this for use on personal computers or laptops (Windows or Apple) but it also works on a smartphone (iPhone or Android).
Smart image detection on a herd of zebras