• search hit 12 of 50
Back to Result List

Multi-Element Deep Learning Using False Detection Images for Training Set -Effective For License Plate Detection-

  • In deep learning, in order to improve learning performance, preprocessing and ingenuity to combine a plurality of discriminators are performed. It can be inferred that it has elements exceeding the set of learning. Therefore, a configuration to combine multiple recognition elements with low loss will be studied. The advance category classification method is expected to narrow the scope of learning in the next stage. Combining elements specialized for FalsePositive/FalseNegative removal after the positive/negative determination is considered to be effective if the accuracy of the subsequent stage is high. We conducted a license plate recognition experiment by combining these and achieved the best performance for Caltech data.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author:Kazuo Ohzeki, Yoshikazu Kido, Yutaka Hirakawa, Stefan-Alexander SchneiderORCiDGND
URL / DOI:https://www.ieice.org/ken/index/ieice-techrep-117-514-e.html
Identifier:0913-5685 OPAC HS OPAC extern
Parent Title (English):IEICE Technical Report
Subtitle (English):Pattern Recognition and Media Understanding
Publisher:IEICE
Document Type:Article
Language:English
Date of Publication (online):2018/03/11
Year of first Publication:2018
Volume:Vol. 117
Issue:514
Number of pages:6 Seiten
First Page:25
Last Page:30
Institutes:Fakultät Elektrotechnik
Research focus:FSP2: Mobilität
Publication Lists:Schneider, Stefan-Alexander
Publication reviewed:begutachtet
Licence (German):Es gilt das deutsche Urheberrecht
Release Date:2021/02/16
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.