CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12456 > Article
Paper
30 November 2022 An object classification and detection method with faster R-CNN based on multiscale anchor box and weighted cross entropy classification
Shengye Wang, Leyuan Gao, Xiao Wang
Author Affiliations +
Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561A (2022) https://doi.org/10.1117/12.2659662
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China
Abstract
To improve the accuracy of object classification and recognition, one of the methods is to add recommendation boxes, recommendation boxes are generated by region proposal algorithms. Because of the inherent ratio, the size of the candidate box generated by Faster RCNN's RPN is usually large, which would easily cause a great number of overflows in sliding search. This is unfriendly for multi-objects detection. To improve the precision of multi-objects detection, we introduce a multiscale proposal box that could predict object bounds and object scores at each position. Then, in order to increase the positive sample range of the foreground, the weighted cross entropy classification function is used for binary classification in the RPN network. In the ROI network, the candidate frame reset algorithm is proposed to realize the position regression of the prediction box and multi-classification of objects, which further improves the accuracy of object detection. The experimental result achieves the mAP of 76.2% on the VOC 07 classic dataset, which is 2.7% higher than the Faster R-CNN. On the VOC 12 test, the mAP of 75.6% is improved by 2.5% compared with the Faster R-CNN.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Shengye Wang, Leyuan Gao, and Xiao Wang "An object classification and detection method with faster R-CNN based on multiscale anchor box and weighted cross entropy classification", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561A (30 November 2022); https://doi.org/10.1117/12.2659662
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Convolution
Detection and tracking algorithms
Feature extraction
Network architectures
Neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top