@inproceedings{c879fc23a0754b77836c0599daa10dd5,
title = "A Comprehensive Review of One-stage Networks for Object Detection",
abstract = "Object detection has always been a hot topic in image processing, which is important in a variety of applications. With the advent of the era of big data and the continuous improvement of hardware computing power, deep learning gets more attention in object detection. One popular branch is regression-based (One-stage) model, which uses a single neural network to directly predict bounding boxes and class probabilities from the entire image by one evaluation. One-stage networks can effectively increase the detection speed. This article mainly describes object detection methods based on regression object detectors (One-stage methods), such as You Only Look Once (YOLO) series and Single Shot Multibox Detector (SSD) series. Then, their applications are briefly introduced. The development trend and future development direction of this type of object detection are discussed in the end.",
keywords = "SSD, YOLO, deep learning, object detection, regression",
author = "Yifan Zhang and Xu Li and Feiyue Wang and Baoguo Wei and Lixin Li",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 ; Conference date: 17-08-2021 Through 19-08-2021",
year = "2021",
month = aug,
day = "17",
doi = "10.1109/ICSPCC52875.2021.9564613",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021",
}