In this paper, a new improved plate detection method which uses genetic algorithm (GA) is proposed. GA randomly scans an input image using a fixed detection window repeatedly, until a region with the highest evaluation score is obtained. The performance of the genetic algorithm is evaluated based on the area coverage of pixels in an input image. It was found that the GA can cover up to 90% of the input image in just less than an average of 50 iterations using 30×130 detection window size, with 20 population members per iteration. Furthermore, the algorithm was tested on a database that contains 1537 car images. Out of these images, more than 98% of the plates were successfully detected. © 2016 IEEE.
Eser Adı (dc.title) | Improved license plate detection using HOG-based features and genetic algorithm |
Yayın Türü (dc.type) | Konferans Bildirisi |
Yazar/lar (dc.contributor.author) | MUHAMMAD, Jawad |
Yazar/lar (dc.contributor.author) | ALTUN, Halis |
DOI Numarası (dc.identifier.doi) | 10.1109/SIU.2016.7495978 |
Atıf Dizini (dc.source.database) | Scopus |
Konu Başlıkları (dc.subject) | License Plate Detection |
Konu Başlıkları (dc.subject) | HOG |
Konu Başlıkları (dc.subject) | Genetic Algorithm |
Yayıncı (dc.publisher) | Institute of Electrical and Electronics Engineers Inc. |
Yayın Tarihi (dc.date.issued) | 2016 |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2020-08-07T12:56:45Z |
Açık Erişim tarihi (dc.date.available) | 2020-08-07T12:56:45Z |
Kaynak (dc.source) | 24th Signal Processing and Communication Application Conference, SIU 2016 |
ISSN (dc.identifier.issn) | 9781509016792 (ISBN) |
Özet (dc.description.abstract) | In this paper, a new improved plate detection method which uses genetic algorithm (GA) is proposed. GA randomly scans an input image using a fixed detection window repeatedly, until a region with the highest evaluation score is obtained. The performance of the genetic algorithm is evaluated based on the area coverage of pixels in an input image. It was found that the GA can cover up to 90% of the input image in just less than an average of 50 iterations using 30×130 detection window size, with 20 population members per iteration. Furthermore, the algorithm was tested on a database that contains 1537 car images. Out of these images, more than 98% of the plates were successfully detected. © 2016 IEEE. |
Yayın Dili (dc.language.iso) | tr |
Tek Biçim Adres (dc.identifier.uri) | http://hdl.handle.net/20.500.12498/3013 |