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Automated License Plate Recognition for Resource-Constrained Environments.

Heshan Padmasiri¹, Jithmi Shashirangana¹, Dulani Meedeniya¹

¹Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This summary is machine-generated.

This study introduces a hardware-efficient automated license plate recognition system for resource-constrained edge devices. The novel deep-learning approach achieves high accuracy under extreme lighting, rivaling server-grade solutions.

Keywords:

edge computing energy efficiency low cost night vision resource-constrained devices

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Area of Science:

Computer Science
Artificial Intelligence
Embedded Systems

Background:

Deep learning in embedded systems boosts edge computing.
Resource-constrained edge devices face limitations with high-end hardware requirements for complex computations.
Existing solutions often lack efficiency for low-power, limited-resource edge applications.

Purpose of the Study:

To present a novel, hardware-efficient automated license plate recognition (ALPR) system.
To demonstrate a proof of concept for ALPR on resource-constrained edge devices.
To address the challenge of implementing advanced AI on low-power edge hardware.

Main Methods:

Developed a novel set of neural networks tailored for diverse hardware configurations.
Implemented a purely edge-based ALPR solution designed for low-resource environments.
Validated models using diverse license plate datasets under varying daytime and nighttime conditions.

Main Results:

Achieved robust performance across extreme illumination changes (day/night).
Demonstrated competitive accuracy and energy efficiency compared to state-of-the-art server-grade systems.
Validated low computational latency and communication overhead for real-time edge deployment.

Conclusions:

The proposed hardware-efficient ALPR system is suitable for resource-constrained edge environments.
Novel neural network designs enable generalizability across different low-cost hardware.
The solution offers a viable alternative to power-hungry, server-based ALPR systems.