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.
| Original language | English |
|---|---|
| Pages (from-to) | 257-276 |
| Number of pages | 20 |
| Journal | Proceedings of the IEEE |
| Volume | 111 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Mar 2023 |
Keywords
- Computer vision
- convolutional neural networks (CNNs)
- deep learning
- object detection
- technical evolution
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