Car_plate_detection_by_darknet_yoloV3_R

Tools could be used:

• Then I explored which language I could use: MATLAB or python
• Another take was on which library I need to choose tensorflow , keras , scikit-learn , pytorch
• How to image could be processed: using opencv
• Frameworks could be used: darknet , darkflow(tensorflow version of darknet) ,LPRNet , Tesseract
• if wanna go directly with an API then it's : GoogleVision

Final product is made using darknet framework, yolov3 weights and used google colab so that it could use cloud's hardware for processing and cloud's storage for storing the dataset using it as IaaS and Sar

Some more explanation related to tools

Core Framework and Tools

• Python is a very popular high-level programming language that is great for data science. Its ease of use and wide support within popular machine learning platforms, coupled with a large catalog of ML libraries, has made it a leader in this space.
• Pandas is an open-source Python library designed for analyzing and manipulating data. It is particularly good for working with tabular data and time-series data.
• NumPy, like Pandas, is a Python library. NumPy provides support for large, multi-dimensional arrays of data, and has many high-level mathematical functions that can be used to perform operations on these arrays.

Machine Learning and Deep Learning

• Scikit-Learn is a Python library designed specifically for machine learning. It is designed to be integrated with other scientific and data-analysis libraries, such as NumPy, SciPy, and matplotlib.
• Apache Spark is an open-source analytics engine that is designed for cluster-computing and that is often used for large-scale data processing and big data.
• TensorFlow is a free, open-source software library for machine learning built by Google Brain.
• Keras is a Python deep-learning library. It provide an Application Programming Interface (API) that can be used to interface with other libraries, such as TensorFlow, in order to program neural networks. Keras is designed for rapid development and experimentation.
• PyTorch is an open source library for machine learning, developed in large part by Facebook's AI Research lab. It is known for being comparatively easy to use, especially for developers already familiar with Python and a Pythonic code style.

YoloV3 functioning:

"Selects box with highest probability from all boxed objects."

Key Summary

• You Look Only Once (Yolo) Implemented in Tensorflow
• K-means clustering across short rolling windows to group similar objects across frames
• Predict if object is similar between frames

Yolo Basics

• Single Feedforward network
• Yolo reframe object detection as single regression problem
• Images -> Pixels -> Bounding Box -> Probabilities
• Divides image into S X S Grid
• If Object centre falls in the Grid then grid cell is responsible for detecting the object
• Predict bounding boxes and class probabilities

Yolo Implementation

• 32 layer Deep CNN
• Input Image resized into 448 x 448
• Yolo Network Output = S x S x (B*5 + C) tensor of predictions ( 7 x 7 x 30 )
• S - Numbers of rows and columns in which we divide the image
• B - Number of objects that can be predicted in given box
• C - Number of classes
• 5 - Terms account for x-axis grid offset, y-axis grid offset, width, height and confidence in each grid cell
• Dataset - Pascal VOC Dataset

K-Means Extension to Yolo

• K-means clustering across images within the short rolling window to group similar objects across frames
• Define Distance between two images I1, I2 given dimensions x,y and color channels c
• Works well when images are similar

Prerequisites

Software Requirements:

• Google colab notebook or jupyter Notebook
• Python 3.8
• OpenCV

Hardware Requirements:

• NVIDIA GPU 940 MX and above
• 12 GB Ram and above

Google Colab Notebooks

For environment set up( installation notebook):

https://colab.research.google.com/drive/1A0QJcqFwNJV0_y7TAE4ZSeB9VVO_3ceb?usp=sharing

For Model training (training notebook) :

https://colab.research.google.com/drive/1jsVbw4L2FKPcB9q4-YMvR6ZEKoQjvaGw?usp=sharing

For using the model over example image( demo notebook):

https://colab.research.google.com/drive/1WnPkQEaDg93u6nMNywZNgZwpIMdkbbMR?usp=sharing

Resources used

Link for dataset:

• http://www.zemris.fer.hr/projects/LicensePlates/english/ (used here)
  
• https://datasetsearch.research.google.com/

link for labelling dataset:

• https://github.com/tzutalin/labelImg (used here)
  

• https://github.com/EscVM/OIDv4_ToolKit
  

• https://github.com/theAIGuysCode/OIDv4_ToolKit
  

• Have used instructions as per above two repos only. Later one only provide with a command at top to provide final output of dataset.

• Further dataset has been directly uploaded to google drive for training the model

• Got error with AWS Client while fetching images as per these repos and had solved that by upgrading.

References

• https://colab.research.google.com/drive/1lTGZsfMaGUpBG4inDIQwIJVW476ibXk_#scrollTo=jAN2TNZ007c_
  
• https://colab.research.google.com/drive/1Mh2HP_Mfxoao6qNFbhfV3u28tG8jAVGk#scrollTo=VHw00Cro8ONr
  

File Repo Reference

• https://github.com/theAIGuysCode/YOLOv3-Cloud-Tutorial/tree/master/yolov3
  
• https://github.com/theAIGuysCode/YOLOv3-Cloud-Tutorial
  
• https://blog.francium.tech/custom-object-training-and-detection-with-yolov3-darknet-and-opencv-41542f2ff44e

Presentation Website

https://komal7209.github.io/project-presentation-website/