Abstract
License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.
Highlights
The detection and recognition of license plates are critical parts of traffic monitoring and are essential to the development of a connected Intelligent transportation system
The images used in this study were acquired using an 8-megapixel smartphone camera with each picture having an average size of 3 MB under various lighting conditions. 700 images of vehicles with Nigerian license plates were captured and digitally augmented by rotation and skewing to provide a dataset of 2100 images to be used in training the plate detection and character recognition algorithms. 200 images were captured and preserved for validating the ALPR system
All model implementations and algorithms were compiled into a single python script and validated in a single step with 200 vehicle and license plate images in the validation set on a general-purpose Personal computer without Graphical Processing Units (GPU)
Summary
The detection and recognition of license plates are critical parts of traffic monitoring and are essential to the development of a connected Intelligent transportation system. Automatic vehicle License Plate Detection and Recognition (ALPR) engines have been extensively explored and have attained state-of-the-art results with the recent use of Graphical Processing Units (GPU) in deep learning computation applications (Hendry & Chen, 2019). These GPU systems are expensive and may not be widely available in developing nations. Deep learning-based license plate detection and recognition systems that achieve exceptional accuracy typically use two-stage deep learning techniques such as R-CNN and FR-CNN for ALPR, which are too heavy to operate in real-time on simple non-GPU devices (Zebin et al, 2019). Character segmentation and character recognition using http://cis.ccsenet.org
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