An Evaluation of Deep Learning Methods for Small Object Detection
Joint Authors
Nguyen, Nhat-Duy
Do, Tien
Ngo, Thanh Duc
Le, Duy-Dinh
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-27
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
Small object detection is an interesting topic in computer vision.
With the rapid development in deep learning, it has drawn attention of several researchers with innovations in approaches to join a race.
These innovations proposed comprise region proposals, divided grid cell, multiscale feature maps, and new loss function.
As a result, performance of object detection has recently had significant improvements.
However, most of the state-of-the-art detectors, both in one-stage and two-stage approaches, have struggled with detecting small objects.
In this study, we evaluate current state-of-the-art models based on deep learning in both approaches such as Fast RCNN, Faster RCNN, RetinaNet, and YOLOv3.
We provide a profound assessment of the advantages and limitations of models.
Specifically, we run models with different backbones on different datasets with multiscale objects to find out what types of objects are suitable for each model along with backbones.
Extensive empirical evaluation was conducted on 2 standard datasets, namely, a small object dataset and a filtered dataset from PASCAL VOC 2007.
Finally, comparative results and analyses are then presented.
American Psychological Association (APA)
Nguyen, Nhat-Duy& Do, Tien& Ngo, Thanh Duc& Le, Duy-Dinh. 2020. An Evaluation of Deep Learning Methods for Small Object Detection. Journal of Electrical and Computer Engineering،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1183880
Modern Language Association (MLA)
Nguyen, Nhat-Duy…[et al.]. An Evaluation of Deep Learning Methods for Small Object Detection. Journal of Electrical and Computer Engineering No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1183880
American Medical Association (AMA)
Nguyen, Nhat-Duy& Do, Tien& Ngo, Thanh Duc& Le, Duy-Dinh. An Evaluation of Deep Learning Methods for Small Object Detection. Journal of Electrical and Computer Engineering. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1183880
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1183880