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Automatic IOT and Machine Learning–Based Toll Collection System for Moving Vehicles

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Accelerating Discoveries in Data Science and Artificial Intelligence II (ICDSAI 2023)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 438))

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Abstract

The proposed work aims to develop an automated toll collection system for charging vehicles passing through a toll plaza. In India, the existing manual toll collection system is slow and leads to heavy traffic congestion and long queues at toll plazas. This causes significant waiting time, fuel wastage, increased pollution, and reduced highway speeds, negatively affecting the quality of food products, especially perishable items like milk. To address these challenges, an effective solution is presented here, focusing on localizing the license plates of vehicles. This automatic license plate recognition approach utilizes various image processing techniques to read the number plate of a moving car. Initially, the captured image is converted to grayscale to optimize memory consumption and processing speed, by selecting a single plane from the RGB (red, green, blue) channels. A camera captures the image of the moving vehicle from a fixed distance throughout the process. To enhance the image quality, morphological filters are applied, followed by optical character recognition (OCR) using a convolutional neural network (CNN). A database has been created and linked to test the performance of the prototype toll collection system. This database contains information such as the number of vehicles, vehicle owners’ names, unique identification numbers, mobile numbers, and linked bank account balances. The recognized characters from the license plate are compared with the database for matching. Once a match is found, the vehicle proceeds through the toll collection process. This involves an Internet of Things (IoT) integration, where barriers open and close automatically based on the system’s response. The main advantages of this system include fast response times and efficient detection. The results demonstrate a significant reduction in vehicle waiting time, queue length, fuel wastage, and pollution at toll plazas.

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Authors and Affiliations

  1. Department of ECE, Lendi Institute of Engineering and Technology, Vizianagaram, India

    B. Sridhar, T. Prathusha, T. Jagadeesh & Y. Swarna Deepika

Authors
  1. B. Sridhar
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  2. T. Prathusha
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  3. T. Jagadeesh
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  4. Y. Swarna Deepika
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Corresponding author

Correspondence to B. Sridhar .

Editor information

Editors and Affiliations

  1. Information & Decision Sciences, College of Business and Public Administration, California State University, San Bernard, San Bernardino, CA, USA

    Frank M. Lin

  2. Computer & Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA

    Ashokkumar Patel

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India

    Bosubabu Sambana

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Sridhar, B., Prathusha, T., Jagadeesh, T., Deepika, Y.S. (2024). Automatic IOT and Machine Learning–Based Toll Collection System for Moving Vehicles. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence II. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-51163-9_33

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