In the previous blog, Introduction to Object detection, we learned the basics of object detection. We also got an overview of the YOLO (You Look Only Once algorithm). In this blog, we will extend our learning and will dive deeper into the YOLO algorithm. We will learn topics such as intersection over area metrics, non maximal suppression, multiple object detection, anchor boxes, etc. Finally, we will build an object detection detection system for a self-driving car using the YOLO algorithm. We will be using the Berkeley driving dataset to train our model.
Before, we get into building the various components of the object detection model, we will perform some preprocessing steps. The preprocessing steps involve resizing the images (according to the input shape accepted by the model) and converting the box coordinates into the appropriate form.