TWO-STAGE CLASSIFICATION ARCHITECTURES IN MODERN OBJECT DETECTION: FROM REGION PROPOSALS TO FINE-GRAINED RECOGNITION

Object detection architectures are broadly divided into single-stage and two-stage families. Single-stage detectors, such as the YOLO and SSD lineages, predict bounding boxes and class labels in a single dense pass over the image, prioritizing speed. Two-stage detectors, beginning with R-CNN and refined through Fast R-CNN, Faster R-CNN, and their many descendants, separate the task into a region proposal stage followed by a classification and refinement stage, generally trading some inference speed for improved localization and classification accuracy. This article examines the architectural principles behind two-stage and cascaded classification pipelines, independent of any single application domain, to clarify why splitting detection into sequential stages has proven so durable as a design pattern. The discussion moves from the basic rationale for splitting localization and classification, through the internal anatomy of the second-stage classifier, to the specific architectural refinements — cascading, head decoupling, and attention — that have been proposed to strengthen it.