# Object Detection in 20 Years: A Survey

Source: [Object Detection in 20 Years: A Survey](<https://www.sota2.com/research/paper/68fa6e631e209131cea3fa1e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:56:59.119Z

Authors: Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, Jieping Ye (3) \_\_INSTITUTION\_5\_\_ Beihang University, (2) Carleton University, (3) Alibaba Group)

Published: 2019-05-13T14:26:50.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.05055>)

[PDF](<https://arxiv.org/pdf/1905.05055.pdf>)