License plate detection with machine learning without using number recognition

• Autorzy: Ohzeki Kazuo , Geigis Max , Schneider Stefan

Identyfikatory

DOI

10.15439/2019F121

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

In autonomous driving, detecting vehicles together with their parts, such as a license plate is important. Many methods with using deep learning detect the license plate based on number recognition. However, there is an idea that the method using deep learning is difficult to use for autonomous driving because of the complexity in realizing deterministic verification. Therefore, development of a method that does not use deep learning (DL) has become important again. Although the authors have made the world's best performance in 2018 for Caltech data with using DL, this concept has now turned to another research without using DL. The CT5L method is the latest type, that includes techniques of the continuity of vertical and horizontal black-and-white pixel values inside the plate, unique Hough transform, only vertical and horizontal lines are detected, the top five in the order of the number of votes to ensure good performance. In this paper, a method to determine the threshold value for binarizing input by machine learning is proposed, and good results are obtained. The detection rate is improved by about 20 points in percent as compared to the fixed case. It achieves the best performance among the conventional fixed threshold method, Otsu's method, and the conventional method of JavaANPR.

Słowa kluczowe

EN

distance measurement; feature extraction; Hough transforms; image segmentation; artificial intelligence; object detection

PL

pomiar odległości; ekstrakcja cech; Transformacja Hougha; segmentacja obrazu; sztuczna inteligencja; wykrywanie obiektów

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 333–--340
• Opis fizyczny: Bibliogr. 30 poz., tab., wykr., rys.

Twórcy

autor

Ohzeki Kazuo

• Algorithm Lab., Nishida Build. 5F 2-14-6 Shibuya, Shibuya-ku 150-0002 Tokyo, Japan

autor

Geigis Max

• University of Applied Science, Bahnhofstraße 61, 87435 Kempten (Allgäu), Germany

autor

Schneider Stefan

• University of Applied Science, Bahnhofstraße 61, 87435 Kempten (Allgäu), Germany

Bibliografia

• 1. Documentation_1st_RoundTableAutonomousDriving.pdf https://www.hs-kempten.de/fileadmin/fh-kempten/HK/news/2016/
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Uwagi

1. Track 2: Computer Science & Systems

2. Technical Session: 12th International Symposium on Multimedia Applications and Processing

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).