Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition

• URL: http://arxiv.org/abs/2408.02904v1
• Date: Tue, 6 Aug 2024 02:27:54 GMT
• Title: Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition
• Authors: M. A. Sayedelahl,
• Abstract summary: This paper introduces a novel two-stage framework for accurate Egyptian Vehicle License Plate Recognition (EVLPR) The first stage employs image processing techniques to reliably localize license plates, while the second stage utilizes a custom-designed deep learning model for robust Arabic character recognition. The proposed system achieves a remarkable 99.3% accuracy on a diverse dataset, surpassing existing approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces a novel two-stage framework for accurate Egyptian Vehicle License Plate Recognition (EVLPR). The first stage employs image processing techniques to reliably localize license plates, while the second stage utilizes a custom-designed deep learning model for robust Arabic character recognition. The proposed system achieves a remarkable 99.3% accuracy on a diverse dataset, surpassing existing approaches. Its potential applications extend to intelligent traffic management, including traffic violation detection and parking optimization. Future research will focus on enhancing the system's capabilities through architectural refinements, expanded datasets, and addressing system dependencies.

Related papers

• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition [49.20086587208214]
  We propose a cross-domain few-shot in-context learning method based on the MLLM for enhancing traffic sign recognition. By using description texts, our method reduces the cross-domain differences between template and real traffic signs. Our approach requires only simple and uniform textual indications, without the need for large-scale traffic sign images and labels.
  arXiv  Detail & Related papers  (2024-07-08T10:51:03Z)
• A Dataset and Model for Realistic License Plate Deblurring [17.52035404373648]
  We introduce the first large-scale license plate deblurring dataset named License Plate Blur (LPBlur) Then, we propose a License Plate Deblurring Generative Adversarial Network (LPDGAN) to tackle the license plate deblurring. Our proposed model outperforms other state-of-the-art motion deblurring methods in realistic license plate deblurring scenarios.
  arXiv  Detail & Related papers  (2024-04-21T14:36:57Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [132.20119288212376]
  We propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously. To the best of our knowledge, we are the first to systematically investigate each part of an interpretable end-to-end vision-based autonomous driving system.
  arXiv  Detail & Related papers  (2022-07-15T16:57:43Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks [0.0]
  We propose a novel segmentation-free framework for license plate recognition and introduce NP-ALPR dataset. The proposed network model consists of the latest deep learning methods and state-of-the-art ideas, and benefits from a novel network architecture. We evaluate the effectiveness of the proposed method on three different datasets and show a recognition accuracy of over 99% and over 70 fps.
  arXiv  Detail & Related papers  (2022-02-21T14:45:03Z)
• Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems [0.024790788944106048]
  Four smart systems are developed to recognize Egyptian vehicles license plates. Two systems are based on character recognition, which are (System1, Characters Recognition with Classical Machine Learning) and (System2, Characters Recognition with Deep Machine Learning) The other two systems are based on the whole plate recognition which are (System3, Whole License Plate Recognition with Classical Machine Learning) and (System4, Whole License Plate Recognition with Deep Machine Learning)
  arXiv  Detail & Related papers  (2021-07-24T15:58:01Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles [9.748996198083425]
  This paper proposes a federated learning based dynamic map fusion framework to achieve high map quality. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform.
  arXiv  Detail & Related papers  (2021-03-05T16:28:46Z)
• Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition [0.0]
  The proposed system uses a novel approach to vehicle tracking using Vehicle License plate detection and recognition (OCR) technique. Results were obtained at a speed of 30 frames per second with accuracy close to human.
  arXiv  Detail & Related papers  (2020-05-10T14:03:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.