Improved Computer Vision-based Framework for Electronic Toll Collection

Mathews, Mary Shaji; Prabu, M.; Pitchai, Arish; Roberts, Derin Ben; Rahul, G

doi:10.1109/confluence52989.2022.9734219

Cited by 1 publication

(1 citation statement)

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“…Automatic License Plate detection and Recognition (ALPR) plays a critical role in intelligent transportation systems. It can find applications in many real-world problems including traffic monitoring [1], electronic toll collection [2] (ETC), intelligent parking lots [3] (IPLs), and elsewhere [4,5]. Due to its prominence and importance, ALPR has attracted great attention over the last two decades (see [6,7] for extensive surveys).…”

Section: Introductionmentioning

confidence: 99%

FAFEnet: A fast and accurate model for automatic license plate detection and recognition

Zhou

Cheng

Jiang

et al. 2022

IET Image Processing

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Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end-to-end CNN-based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN-based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real-world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency.

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