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Automatic Number Plate Detection and Recognition System for Small-Sized Number Plates of Category L-Vehicles for
Hafiz Hashim Imtiaz1, Paul Schaffer1, Paul Hesse1
1Institute of Electrical Measurement and Sensor Systems, Graz University of Technology, 8010 Graz, Austria.
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Road traffic emissions are still a significant contributor to air pollution, which causes adverse health effects. Remote emission sensing (RES) is a state-of-the-art technique that continuously monitors the emissions of thousands of vehicles in traffic. Automatic number plate recognition (ANPR) systems are an essential part of RES systems to identify the registered owners of high-emitting vehicles. Recognizing number plates on L-vehicles (two-wheelers) with a standard ANPR system is challenging due to differences in size and placement across various categories. No ANPR system is designed explicitly for Category L vehicles, especially mopeds. In this work, we present an automatic number plate detection and recognition system for Category L vehicles (L-ANPR) specially developed to recognize L-vehicle number plates of various sizes and colors from different categories and countries. The cost-effective and energy efficient L-ANPR system was implemented on roads during remote emission measurement campaigns in multiple European cities and tested with hundreds of vehicles. The L-ANPR system recognizes Category L vehicles by calculating the size of each passing vehicle using photoelectric sensors. It can then trigger the L-ANPR detection system, which begins detecting license plates and recognizing license plate numbers with the L-ANPR recognizing system. The L-ANPR system's license plate detection model is trained using thousands of images of license plates from various types of Category L vehicles across different countries, and the overall detection accuracy with test images exceeded 90%. The L-ANPR system's character recognition is designed to identify large characters on standard number plates as well as smaller characters in various colors on small, moped license plates, achieving a recognition accuracy surpassing 70%. The reasons for false recognitions are identified and the solutions are discussed in detail.
