As Intelligent Transportation Systems applications increase in prevalence, Automatic License Plate Recognition solutions must be made continually faster and more accurate. The authors propose an embedded system for fast and accurate license plate segmentation and recognition using a modified single shot detector (SSD) with a feature extractor based on depthwise separable convolutions and linear bottlenecks. The feature extractor requires less parameters than the original SSD + VGG implementation, enabling fast inference. Tested on the Caltech Cars dataset, the proposed model achieves 96.46% segmentation and 96.23% recognition accuracy. Tested on the UCSD‐Stills dataset, the proposed model achieves 99.79% segmentation and 99.79% recognition accuracy. The authors achieve a per‐plate (resized to 300 × 300 px) processing time of 59 ms on an Intel Xeon CPU with 12 cores (2.60 GHz per core), 14 ms using the same CPU and OpenVINO (a neural network acceleration platform), and 66 ms using the proposed low‐cost Raspberry Pi 3 and Intel Neural Compute Stick 2 with OpenVINO embedded system.
License plate segmentation and recognition system using deep learning and OpenVINO
IET Intelligent Transport Systems ; 14 , 2 ; 119-126
2020-02-01
8 pages
Article (Journal)
Electronic Resource
English
image segmentation , automatic license plate recognition solutions , embedded systems , feature extractor , single shot detector , object detection , OpenVINO , license plate segmentation and recognition system , traffic engineering computing , feature extraction , deep learning , UCSD‐Stills dataset , image recognition , learning (artificial intelligence) , depthwise separable convolutions , embedded system , convolutional neural nets , intelligent transportation systems , recognition system , Caltech Cars dataset , recognition accuracy
License plate segmentation and recognition system using deep learning and OpenVINO
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