International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 1871 - 1879

Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection

Authors
Jun Zhang1, Feiteng Han1, *, Yutong Chun1, *, ORCID, Kangwei Liu2, Wang Chen3
1School of Management and Engineering, Capital University of Economics and Business, No. 121, Zhangjia Road, Huaxiang, Fengtai District, Beijing, 100070, China
2Alibaba DAMO Academy, District 4, Wangjing East Park, Chaoyang District, Beijing, 100102, China
3School of Information, Renmin University of China, No.59 Zhongguancun Road, Haidian District, Beijing, 100872, China
*Corresponding authors. Email: h_feiteng@163.com; chunyutong@yeah.net
Corresponding Authors
Feiteng Han, Yutong Chun
Received 8 October 2020, Accepted 31 May 2021, Available Online 28 June 2021.
DOI
https://doi.org/10.2991/ijcis.d.210622.003How to use a DOI?
Keywords
Data augmentation; Object detection; Convolutional neural network; Deep learning
Abstract

Data augmentation is an important technique to improve the performance of deep learning models in many vision tasks such as object detection. Recently, some works proposed the copy-paste method, which augments training dataset by copying foreground objects and pasting them on background images. By designing a learning-based context model to predict realistic placement regions, these approaches have been proved to be more effective than traditional data augmentation methods. However, the performance of the existing context model was limited by three problems: (1) The definitions of positive and negative samples generate too much label noise. (2) The examples with masked regions lose a lot of context information. (3) The sizes (i.e., scale and aspect ratios) of predicted regions are sampled from a prior shape distribution, which leads to a coarse estimation. In this work, we first explore the placement rules that generate realism and effective training examples for detectors. And then, we propose a trainable context model in order to find proper placement regions by classifying and refining dense prior default boxes. We also design a corresponding reasonable generation for training examples by annotating ground truth on free space according to the placement rules. The experimental results on PASCAL VOC show that our approach outperforms the state-of-the-art- related work.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1871 - 1879
Publication Date
2021/06/28
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.210622.003How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Jun Zhang
AU  - Feiteng Han
AU  - Yutong Chun
AU  - Kangwei Liu
AU  - Wang Chen
PY  - 2021
DA  - 2021/06/28
TI  - Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection
JO  - International Journal of Computational Intelligence Systems
SP  - 1871
EP  - 1879
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210622.003
DO  - https://doi.org/10.2991/ijcis.d.210622.003
ID  - Zhang2021
ER  -