In this chapter, we created a new application named Detective to detect objects using different approaches. First, we used an OpenCV built-in cascade classifier to detect the faces of cats. Then we learned how to train cascade classifiers by ourselves. We trained a cascade classifier for a rigid object (a no-entry traffic sign) and a cascade classifier for a less rigid object (the faces of Boston Bulls), then tested this with our application.
We moved on to the deep learning approach. We talked about the increasingly expanding deep learning technology, introduced many frameworks, and learned about the different ways in which a DNN model may detect objects using two-stage detectors and one-stage detectors. We combined the DNN module of the OpenCV library and the pretrained YOLOv3 model to detect objects in our application.
At the end, we talked about real time and the performance...
