Object Detection in 20 Years: A Survey

TL;DR

This survey comprehensively reviews two decades of object detection research, highlighting technological evolution, key milestones, datasets, metrics, and recent advances driven by deep learning in computer vision.

Contribution

It provides an extensive overview of the historical development, technical innovations, and current state-of-the-art methods in object detection over 25 years.

Findings

Identification of key milestone detectors in history

Analysis of evolution in detection datasets and metrics

Overview of recent state-of-the-art detection techniques

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 paper 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 paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods.