4.7 Article

Object Detection in 20 Years: A Survey

Journal

PROCEEDINGS OF THE IEEE
Volume 111, Issue 3, Pages 257-276

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2023.3238524

Keywords

Object detection; Detectors; Computer vision; Feature extraction; Deep learning; Convolutional neural networks; convolutional neural networks (CNNs); deep learning; object detection; technical evolution

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Object detection, a fundamental problem in computer vision, has received significant attention in recent years. This article reviews the rapid technological evolution of object detection over the past two decades and its impact on the entire computer vision field. It covers various topics such as milestone detectors, datasets, metrics, fundamental building blocks, speedup techniques, and state-of-the-art methods.
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.

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