So how do we go about detecting these rotated objects? Well, firstly, we need to train our model on some data this could be images of aerial views from drones or satellites, with labeled ground truths for the locations and orientations of any objects that might be present.
Once we have our training data sorted out, we can use a technique called “rotated bounding boxes” to identify these rotated objects. This involves creating rectangular boxes around each object in an image, but allowing them to be tilted or skewed depending on the orientation of the object itself.
For example, let’s say we have an image that looks like this:
[Insert image here]
In this case, there are a few buildings and trees scattered throughout the scene some of which might be rotated due to wind or other factors. To detect these objects using our model, we would first feed the image through a convolutional neural network (CNN) that has been trained on similar data.
The CNN will then output a set of predictions for each possible location and orientation in the image this could be hundreds or thousands of potential bounding boxes, depending on how many objects are present and how large they might be.
From there, we can use some fancy math to calculate which of these bounding boxes is most likely to contain a rotated object this involves comparing the predicted orientation of each box with the actual orientation of any ground truth labels that might be available for that particular image.
Once we have identified our rotated objects, we can then extract features from them using another CNN or similar technique. These features could include things like color, texture, and shape all of which are important for identifying different types of objects in an aerial view.
Finally, we can use these extracted features to classify each object as either “building” or “tree”, depending on what it might be. This is where our model really shines by using rotated bounding boxes and other advanced techniques, we’re able to accurately identify objects that would otherwise be difficult or impossible to detect using traditional methods.
Rotated object detection in aerial images not as complicated as it might seem at first glance.