Non-Max Suppression In this post, we will learn how the non-max suppression algorithm allows us to overcome multiple detections of the same object in an image. Let’s go through an example! Let’s say we want to detect pedestrians, cars, and motorcycles in this image. If we look at the picture above we can see that there are two cars. Each of these two cars has one midpoint so it should be assigned to just one…
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Intersection over Union In this post, we will learn about a function called Intersection over union. We will use this to make our object detection algorithm to work even better. How do we tell if our object detection algorithm is working well? When doing the object detection our task is to localize the object in the best possible way. Take a look at the picture above we can see that there are two bounding boxes…
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Bounding box predictions In the last post, we learned how to use a convolutional implementation of sliding windows. That’s more computationally efficient, but it still has a problem of not outputting the most accurate bounding boxes. In this post, we will see how we can obtain more accurate predictions of bounding boxes. Output accurate bounding boxes With sliding windows, we take the sets of windows that we move throughout the image and we obtain a set of sliding…
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Convolutional operation of sliding windows In the previous post we learned about the sliding windows object detection algorithm using a \(convnet \), but we saw that it was too slow. In this post we will see how to implement that algorithm convolutionaly. Let’s see what that means. To build up the convolutional implementation of sliding windows, let’s first see how we can turn \(Fully \enspace connected \) layers in our neural network into \(Convolutional\) layes.…
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Object Detection We have learned about object localization as well as landmark detection, now let’s build an object detection algorithm. In this post we’ll learn how to use a \(convnet \) to perform object detection using a Sliding windows detection algorithm. Car detection – an example Let’s say we want to build a car detection algorithm. Some examples of a training set We can first create a labeled training set \((x,y) \) with closely cropped…
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