An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks

Taleb, Hamdan; Li, Zhipeng; Yuan, Changan; Wu, Hongjie; Zhao, Xing‐Ming; Ghanem, Fahd A.

doi:10.1007/978-3-031-13832-4_26

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Also, areas of further improvement of some of the selected CNN-based techniques were suggested. There are several studies that use YOLOv5 5,6 for license plate detection [14][15][16][17][18][19][20] and LPRNet 7 or other OCR techniques 21 for license plate recognition. For example, a recent study published in 2022 proposes a solution for LPR and vehicle re-identification for surveillance systems.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous license plate recognition and face detection application at the edge

Alim

Kavakli

Okcu

et al. 2023

AI and Optical Data Sciences IV

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Face recognition (FR) and license plate recognition (LPR) are very crucial algorithms for identification of humans and vehicles in several applications such as surveillance, traffic and access-control. The advances in small singleboard computers with high parallel processing power capabilities and the use of low-power Neural Processing Units (NPU) inside embedded System on Chips (SoC), enable real-time face detection (FD) and LPR at the edge. On the other hand, it is still a challenge to run multiple algorithms concurrently with high accuracy and prompt execution (high frame rates) that requires a very efficient software/video analytics algorithm development. Both FR and LPR algorithms need two-stage processing that involve detection and recognition. In this study, we propose a method that enables simultaneous face detection associated with landmark and quality information and LPR at the edge. The FD pipeline detects and tracks the faces, extracts landmarks and quality of faces, to select appropriate faces for recognition and then sends them to face recognition server. LPR algorithm consecutively performs detection and recognition on the embedded platform. Extended YOLO model is utilized for face selection while pruned YOLO and LPRNet models are exploited for license plate detection and license plate reading, respectively. In order to enable real-time performance with high accuracy; optimized AI-models and software architecture are used. As a result of this study, we obtain a high-performance, high-precision and real-time combined face/LPR recognition system which can be very useful for surveillance and security applications.

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