This repository for project that can Automate Number Plate Recognition (ANPR) in Morocco Licensed Vehicles. πŸ’» + πŸš™ + πŸ‡²πŸ‡¦ = πŸ€– πŸ•΅πŸ»β€β™‚οΈ

Overview

MoroccoAI Data Challenge (Edition #001)

This Reposotory is result of our work in the comepetiton organized by MoroccoAI in the context of the first MoroccoAI Data Challenge. For More Information, check the Kaggle Competetion page !

Automatic Number Plate Recognition (ANPR) in Morocco Licensed Vehicles

In Morocco, the number of registered vehicles doubled between 2000 and 2019. In 2019, a few months before lockdowns due to the Coronavirus Pandemic, 8 road fatalities were recorded per 10 000 registered vehicles. This rate is extremely high when compared with other IRTAD countries. The National Road Safety Agency (NARSA) established the road safety strategy 2017-26 with the main target to reduce the number of road deaths by 50% between 2015 and 2026 [1]. Law enforcement, speed limit enforcement and traffic control are one of most efficient measures taken by the authorities to achieve modern road user safety. Automatic Number Plate Recognition (ANPR) is used by the police around the world for law and speed limit enforcement and traffic control purposes, including to check if a vehicle is registered or licensed. It is also used as a method of cataloguing the movements of traffic by highways agencies. ANPR uses optical character recognition (OCR) to read vehicles’ license plates from images. This is very challenging for many reasons including non-standardized license plate formats, complex image acquisition scenes, camera conditions, environmental conditions, indoor/outdoor or day/night shots, etc. This data-challenge addresses the problem of ANPR in Morocco licensed vehicles. Based on a small training dataset of 450 labeled car images, the participants have to provide models able to accurately recognize the plate numbers of Morocco licensed vehicles.

Table of Contents

Dataset

The dataset is 654 jpg pictures of the front or back of vehicles showing the license plate. They are of different sizes and are mostly cars. The plate license follows Moroccan standard.

For each plate corresponds a string (series of numbers and latin characters) labeled manually. The plate strings could contain a series of numbers and latin letters of different length. Because letters in Morocco license plate standard are Arabic letters, we will consider the following transliteration: a <=> Ψ£, b <=> Ψ¨, j <=> Ψ¬ (jamaa), d <=> Ψ― , h <=> Ω‡ , waw <=> و, w <=> w (newly licensed cars), p <=> Ψ΄ (police), fx <=> Ω‚ Ψ³ (auxiliary forces), far <=> Ω‚ Ω… Ω… (royal army forces), m <=>Ψ§Ω„Ω…ΨΊΨ±Ψ¨, m <=>M. For example:

  • the string β€œ123Ψ¨45” have to be converted to β€œ12345b”,
  • the string β€œ123و4567” to β€œ1234567waw”,
  • the string β€œ12و4567” to β€œ1234567waw”,
  • the string β€œ1234567ww” to β€œ1234567ww”, (remain the same)
  • the string β€œ1234567far” to β€œ1234567Ω‚ Ω… م”,
  • the string β€œ1234567m” to β€œ1234567Ψ§Ω„Ω…ΨΊΨ±Ψ¨",
  • etc.

We offer the plate strings of 450 images (training set). The remaining 204 unlabeled images will be the test set. The participants are asked to provide the plate strings in the test set.
image

Our Approach

Our approach was to use Object Detection to detect plate characters from images. We have chosen to build two models separately instead of using libraries directly like easyOCR or Tesseract due to its weaknesses in handling the variance in the shapes of Moroccan License plates. The first model was trained to detect the licence plate to be then cropped from the original image, which will be then passed into the second model that was trained to detect the characters.

  • Data acquisition and preparation

    First we start by annotating the dataset on our own using a tool called LabelImg. Then we found that the dataset provided by MSDA Lab was publicly available and fits our approach, as they have prepared the annotation in the following form :

    • A folder that contains the Original image and bounding boxes of plates with 2 format Pascal Voc Format and Yolo Darknet Format.
    • And the other folder , contains only the licence plates and the characters bounding boxes with the same formats.
  • Library and Model Architecture

    We have choose faster-rcnn model for both Object detection tasks, using library called detectron2 based on Pytorch and developed by FaceBook AI Research Laboratory (FAIR). A Faster R-CNN object detection network is composed of a feature extraction network which is typically a pretrained CNN, similar to what we had used for its predecessor. This is then followed by two subnetworks which are trainable. The first is a Region Proposal Network (RPN), which is, as its name suggests, used to generate object proposals and the second is used to predict the actual class of the object. So the primary differentiator for Faster R-CNN is the RPN which is inserted after the last convolutional layer. This is trained to produce region proposals directly without the need for any external mechanism like Selective Search. After this we use ROI pooling and an upstream classifier and bounding box regressor similar to Fast R-CNN.

  • Modeling

Training a first Faster-RCNN model only to detect licence plates.

And a second trained separately only to detect characters on cropped images of the licence plates.

The both models were pretrained on the COCO dataset, because we didn’t have enough data, therefor it would only make sense to take the advantage of transfer learning of models that were trained on such a rich dataset.

  • Post-Processing
    Now we have a good model that can detect the majority of the characters in Licence Plates, the work is not done yet, because our model returns the boxes of detected characters, without taking the order in consideration. So we had to do a post-processing algorithm that can return the licence plate characters in the right order.
    1. Split characters based on median of Y-Min of all detected characters boxes, by taking characters where their Y-Max is smaller than Median-Y-Mins into a string called top-characters, and those who have Y-Max greater than Median-Y-Mins will be in bottom_characters.
    2. Order characters in top and bottom list from left to right based on the X_Min of the detected Box of each character.

Owner
SAFOINE EL KHABICH
SAFOINE EL KHABICH
Introduction to AI assignment 1 HCM University of Technology, term 211

Sokoban Bot Introduction to AI assignment 1 HCM University of Technology, term 211 Abstract This is basically a solver for Sokoban game using Breadth-

Quang Minh 4 Dec 12, 2022
An open source Jetson Nano baseboard and tools to design your own.

My Jetson Nano Baseboard This basic baseboard gives the user the foundation and the flexibility to design their own baseboard for the Jetson Nano. It

NVIDIA AI IOT 57 Dec 29, 2022
Notspot robot simulation - Python version

Notspot robot simulation - Python version This repository contains all the files and code needed to simulate the notspot quadrupedal robot using Gazeb

50 Sep 26, 2022
Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial

Computational Social Affective Neuroscience Laboratory 147 Jan 06, 2023
[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'

[CVPR2021] Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator Overview This is the entire codebase for the paper

35 Dec 01, 2022
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 06, 2022
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"

mythbuster 27 Dec 23, 2022
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

JoΓ£o Fonseca 3 Jan 03, 2023
AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

AsymmetricGAN for Image-to-Image Translation AsymmetricGAN Framework for Multi-Domain Image-to-Image Translation AsymmetricGAN Framework for Hand Gest

Hao Tang 42 Jan 15, 2022
Transformer Huffman coding - Complete Huffman coding through transformer

Transformer_Huffman_coding Complete Huffman coding through transformer 2022/2/19

3 May 19, 2022
Hcpy - Interface with Home Connect appliances in Python

Interface with Home Connect appliances in Python This is a very, very beta inter

Trammell Hudson 116 Dec 27, 2022
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition Paper: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition accepted fo

64 Dec 18, 2022
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR2021)

NExT-QA We reproduce some SOTA VideoQA methods to provide benchmark results for our NExT-QA dataset accepted to CVPR2021 (with 1 'Strong Accept' and 2

Junbin Xiao 50 Nov 24, 2022
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-

tensordiffeq 74 Dec 09, 2022
An implementation of a sequence to sequence neural network using an encoder-decoder

Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a

Luke Tonin 195 Dec 17, 2022
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
Indices Matter: Learning to Index for Deep Image Matting

IndexNet Matting This repository includes the official implementation of IndexNet Matting for deep image matting, presented in our paper: Indices Matt

Hao Lu 357 Nov 26, 2022
Hepsiburada - Hepsiburada Urun Bilgisi Cekme

Hepsiburada Urun Bilgisi Cekme from hepsiburada import Marka nike = Marka("nike"

Ilker Manap 8 Oct 26, 2022
Open source Python implementation of the HDR+ photography pipeline

hdrplus-python Open source Python implementation of the HDR+ photography pipeline, originally developped by Google and presented in a 2016 article. Th

77 Jan 05, 2023