Detection of license plates by means of a cascade classifier model based on Python language
Detección de placas vehiculares mediante modelo de clasificador en cascada basado en lenguaje Python
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The detection of vehicle plates using machine learning techniques improves the processes of tracking, tracing and security. The development of a cascade classifier model for license plate detection is presented, using Python, OpenCV and Cascade Trainer GUI tools, based on open source. The images used for the processing were captured by a Raspberry Pi camera connected to the embedded plate, in several points of the central area of the frontier city of Cúcuta, Colombia, then sent to a personal computer and redirected through geometric transformations; and to guarantee the high performance of the classification system, data enhancement processes are applied, going from 245 to 1867 images for the training of the cascade detector. The classification model took 17.4 minutes to create, and was tested with images and videos in real environments in the city of Cúcuta, achieving the detection of Colombian and Venezuelan license plates with an effectiveness of 90.26 %.
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