License plate detection for multi-national vehicles: An illumination invariant approach in multi-lane environment. (September 2019)

• Record Type: Journal Article
• Authors: Asif, Muhammad Rizwan; Qi, Chun; Wang, Tiexiang; Fareed, Muhammad Sadiq; Raza, Syed Ali
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 132
• Page End: 147
• Publication Date: 2019-09
• Subjects: Intelligent traffic system -- License plate detection -- Multi-national vehicles -- Corona effect -- Vehicle identification -- Vehicle rear lights; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: License plate detection for multi-national vehicles in a multi-lane scenario. Ability to deal with multi-national license plates of various colors and styles. Capability to handle illumination variance. Performance evaluation on datasets of China, Thailand, Pakistan, USA, UAE and Netherlands. Superior performance as compared to conventional and deep learning approaches. Abstract: Only a few methods in literature are effective for multi-national license plate detection in a multi-lane scenario. These methods are prone to illumination variance, complex background and weak-edged license plates. In this paper, we propose a novel illumination invariant method to handle multi-national vehicle license plates of different colors and styles. Red corona is initially used to detect the tail-lights of vehicles to establish region-of-interest as the license plates are in a vicinity of its tail-lights. The vertical edges within each region-of-interest are obtained using a unique approach that preserve license plate edges for improved performance. Heuristic energy map is then used to distinguish the license plate area. To validate the detected regions, high-level features extracted from AlexNet Convolutional Neural Network are used. Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance, but also outperforms the conventional and deep-learning methods.