Object Detection in 20 Years: A Survey

Zhengxia Zou✉ 1,2
Keyan Chen1,2,3,4
Zhenwei Shi 1,2,3,4
Yuhong Guo5
Jieping Ye✉ 6

Beihang University1
Shanghai Artificial Intelligence Laboratory2
State Key Laboratory of Virtual Reality Technology and Systems3
Beijing Key Laboratory of Digital Media4
Carleton University5
Alibaba Group6
Code [GitHub]
Paper [PDF]
Cite [BibTeX]


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.


A road map of object detection. Milestone detectors in this figure: VJ Det., HOG Det., DPM, RCNN, SPPNet, Fast RCNN, Faster RCNN, YOLO, SSD, FPN, Retina-Net, CornerNet, CenterNet, DETR.