Convolutional neural networks and image processing

The classical machine learning models are quite powerful, but they are limited in their input. We need to pre-process it so that it’s a set of feature vectors. They are also limited in their ability to learn – they are one-shot learners. We can only train them once and we cannot add more training. If more training is required, we need to train these models from the very beginning.

The classical machine learning models are also considered to be rather limited in their ability to handle complex structures, such as images. Images, as we have learned before, have at least two different dimensions and they can have three channels of information – red, green, and blue. In more complex applications, the images can contain data from LiDAR or geospatial data that can provide meta-information about the images.

So, to handle images, more complex models are needed. One of these models is the YOLO model. It’s considered...