Understanding DNN-Based Object Detectors

Unlike image classifiers, which merely report on the most important objects within or attributes of an image, object detectors determine where objects of interest are located within an image. Consequently, object detectors are central to many computer vision applications including (but not limited to) autonomous vehicles and virtual reality. In this presentation, we provide a technical introduction to deep-neural-network-based object detectors. We explain how these algorithms work, and how they have evolved in recent years, utilizing examples of popular object detectors. We examine some of the trade-offs to consider when selecting an object detector for an application and touch on accuracy measurement. We also discuss performance comparison among the models discussed in this presentation.