skip to main content
10.1145/3297280.3297582acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

An efficient deep learning platform for detecting objects

Published: 08 April 2019 Publication History

Abstract

Real-time object detection models based on deep learning are being studied. However, when using deep learning, the user must directly select one of the various object detection models, and the result of object detection may vary depending on the selected object detection model. Therefore, in this paper, we propose an efficient deep learning platform for object detection technology. The proposed platform estimates learning results based on benchmark results and recommends proper object detection model based on deep learning to minimizes user intervention.

References

[1]
Faizan Shaikh, 2018, "Understanding and Building an Object Detection Model from Scratch in Python", Retrieved December 10, 2018 from https://www.analyticsvidhya.com/blog/2018/06/understanding-building-object-detection-model-python/
[2]
Joyce Xu, 2017, "Deep Learning for Object Detection: A Comprehensive Review", Retrieved December 10, 2018 from https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9
[3]
Junwei Han, Dingwen Zhang, Gong Cheng, Nian Liu, and Dong Xu, "Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection", The IEEE Signal Processing Magazine, Vol. 35, No. 1, 84--100, IEEE, 2018.
[4]
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580--587, IEEE, 2014.
[5]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", The IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, 1137--1149, IEEE, 2017.
[6]
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick, "Mask R-CNN", The IEEE International Conference on Computer Vision (ICCV), 2980--2988, IEEE, 2017.
[7]
Tsung-Yi Lin, Priyal Goyal, Ross Girshick, Kaiming He, and Piotr Dollar, "Focal Loss for Dense Object Detection", The IEEE International Conference on Computer Vision (ICCV), 2999--3007, IEEE, 2017.
[8]
Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779--788, IEEE, 2016.
[9]
Joseph Redmon, and Ali Farhadi, "YOLO9000: Better, Faster, Stronger", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517--6525, IEEE, 2017.
[10]
Joseph Redmon, and Ali Farhadi, "YOLOv3: An Incremental Improvement", arXiv Preprint, arXiv:1804.02767, 2017.
[11]
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg, "SSD: Single Shot MultiBox Detector", The European Conference on Computer Vision (ECCV), 21--37, Springer, 2016.
[12]
Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C. Berg, "DSSD: Deconvolutional Single Shot Detector", arXiv Preprint. arXiv:1701.06659, 2017.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
April 2019
2682 pages
ISBN:9781450359337
DOI:10.1145/3297280
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2019

Check for updates

Author Tags

  1. convolutional neural network
  2. object detection
  3. object recognition

Qualifiers

  • Poster

Funding Sources

  • Ministry of Science, ICT
  • Ministry of Education

Conference

SAC '19
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 254
    Total Downloads
  • Downloads (Last 12 months)7
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media