Practical Machine Learning for Computer Vision

Chapter 4. Object Detection and Image Segmentation

So far in this book, we have looked at a variety of machine learning architectures but used them to solve only one type of problem—that of classifying (or regressing) an entire image. In this chapter, we discuss three new vision problems: object detection, instance segmentation, and whole-scene semantic segmentation (Figure 4-1). Other more advanced vision problems like image generation, counting, pose estimation, and generative models are covered in Chapters 11 and 12.

Figure 4-1. From left to right: object detection, instance segmentation, and whole-scene semantic segmentation. Images from Arthropods and Cityscapes datasets.

Tip

The code for this chapter is in the 04_detect_segment folder of the book’s GitHub repository. We will provide file names for code samples and notebooks where applicable.

Object Detection

Seeing is, for most of us, so effortless that, as we glimpse a butterfly from the corner of our eye and turn our head to enjoy its beauty, we don’t even think about the millions of visual cells and neurons at play, capturing light, decoding the signals, and processing them into higher and higher levels of abstraction.

We saw in Chapter 3 how image recognition in ML works. However, the models presented in that chapter were built to classify an image as whole—they could not tell us where in the image a flower was. In ...