微信服务号

微信订阅号

2025-4-6- 4
Home > Archive>Volume 32, Issue 4, 2021 >1201-1227. DOI:10.13328/j.cnki.jos.006166
PDF HTML XML Export Cite reminder
Deep Learning for Multi-scale Object Detection: A Survey
Author:
Affiliation:

Fund Project:

National Key Research and Development Program of China (2016YFB1001200); National Natural Science Foundation of China (61872346)

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    Object detection is a classic computer vision task which aims to detect multiple objects of certain classes within a given image by bounding-box-level localization. With the rapid development of neural network technology and the birth of R-CNN detector as a milestone, a series of deep-learning-based object detectors have been developed in recent years, showing the overwhelming speed and accuracy advantage against traditional algorithms. However, how to precisely detect objects in large scale variance, also known as the scale problem, still remains a great challenge even for the deep learning methods, while many scholars have made several contributions to it over the last few years. Although there are already dozens of surveys focusing on the summarization of deep-learning-based object detectors in several aspects including algorithm procedure, network structure, training and datasets, very few of them concentrate on the methods of multi-scale object detection. Therefore, this paper firstly review the foundation of the deep-learning-based detectors in two main streams, including the two-stage detectors like R-CNN and one-stage detectors like YOLO and SSD. Then, the effective approaches are discussed to address the scale problems including most commonly used image pyramids, in-network feature pyramids, etc. At last, the current situations of the multi-scale object detection are concluded and the future research directions are looked ahead.

    Reference
    Related
    Cited by
Get Citation

陈科圻,朱志亮,邓小明,马翠霞,王宏安.多尺度目标检测的深度学习研究综述.软件学报,2021,32(4):1201-1227

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:August 10,2020
  • Revised:September 20,2020
  • Online: December 02,2020
  • Published: April 06,2021
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2033290Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063