Object Detection in 20 Years: A Survey

TLDR

Object detection is a core, challenging computer vision problem that has attracted intense research and evolved rapidly over the past two decades, especially with deep learning breakthroughs. This survey reviews the technical evolution of object detection from the 1990s to 2022. It covers milestone detectors, datasets, metrics, system building blocks, speedup techniques, and recent state‑of‑the‑art methods.

Abstract

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today's object detection technique as a revolution driven by deep learning, then, back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered in this article, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speedup techniques, and recent state-of-the-art detection methods.