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EyeRoad: Vehicles and plates detector and tracking with string plate recognition

This repository contain the project for Vision and Perception exam of Master in Artificial in Intelligence and Robotics from University La Sapienza of Rome.

The string plate recognition part of this project has been developed and uploaded by Luigi Gallo, co-worker of this project, on the following repository https://github.com/luigi-ga/ALPRNet.git.

Visuals

Alt Text

From this link you can download the full demo video.

Description

The project implements the following features:

  • object detector of vehicles and plates based on FasterRCNN_ResNet50 of PyTorch:
    • By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks". [PDF]
  • string plate recognition based on ALPRNet (imported as sub-module from https://github.com/luigi-ga/ALPRNet.git)
  • tracking and counting of vehicles based on string plate recognition
  • velocity estimation based on domain knowledge

The following pseudocode shows the inference stage of this project with reference to real_time_object_detector.py file.

The training stage is not explained here, but if you are interested check the following files: dataset.py, model.py, training.py and notebook.py:

  • in dataset.py there are classes and functions used to import dataset and create dataloaders
  • in model.py there is the import of the pretrained model from PyTorch
  • in training.py there is the training procedure

The file notebook.py was used as main to perform both training and inference.

for frame in video:
  
  # use the FasterRCNNResNet50 model in inference mode to perform object detection of 
  # vehicles and plates using the frame image
  bounding_boxes_vehicles_and_plates = FasterRCNNResNet50(frame)

  # discard all bounding boxes of plates and apply non-maximum-suppresion and 
  # score-thresholding on bounding boxes of vehicles
  bounding_boxes_vehicles = decode_preditcion_vehicles(bounding_boxes_vehicles_and_plates)

  for bounding_box_vehicle in bounding_boxes_vehicles:

    # crop the image of vehicle using the bounding box
    cropped_vehicle = frame[bounding_box_vehicle]

    # use the FasterRCNNResNet50 model in inference mode to perform object detection of 
    # vehicles and plates using the cropped image
    bounding_boxes_vehicles_plates = FasterRCNNResNet50(cropped_vehicle)

    # discard all bounding boxes of vehicles (there should be no bb of vehicles at 
    # this stage) and extraxt the bounding box of the plate with the highest score, 
    # applying score-thresholding
    bounding_box_plate = decode_prediction_plates(bounding_boxes_vehicles_plates)

    # crop the image of plate using the bounding box
    cropped_plate = cropped_vehicle[bounding_box_plate]
  
    # apply super resolution model on cropped plate to obtain a better image
    sr_cropped_plate = EDSR(cropped_plate)

    # transfrom the sr_cropped_plate in a gray image
    gray_sr_cropped_plate = to_gray(sr_cropped_plate)

    # use ALPRNet model in inference mode to extract the string of the plate
    plate_string = ALPRNet(gray_sr_cropped_plate)

    # use the plate string to perform tracking on the vehicle with that plate
    idx = tracking(plate_string, bounding_box_vehicle)

    # detect the velocity of this vehicle
    velocity_detected = velocity_detector(idx, bounding_box_vehicle)

For the super resolution of the plate was used the EDSR model implemented in OpenCV:

  • Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. [PDF]
  • Official repository GitHub
  • OpenCV documentation

Directory tree

EyeRoad
└── src
    ├── ALPRNet (sub-module)
    ├── modules
    │   ├── dataset.py
    │   ├── detect_plate_string.py
    │   ├── inference.py
    │   ├── model.py
    │   ├── real_time_object_detector.py
    │   └── training.py
    ├── utils
    │   └── frames_to_video.py 
    ├── notebook.ipynb
    └── test.py

Installation

  1. Clone this repository:
git clone --recurse-submodules https://github.com/ValerioSpagnoli/EyeRoad.git
cd EyeRoad
  1. Install the required dependencies using pip:
pip install -r requirements.txt

Usage

Download the weights following the instructions in Download weights.

From this link download the test video, and put it into a folder named video_test in the main directory of this repository. The directory three must be:

EyEroad
└── src
│   └── ...
└── video_test
    └── video_test.mp4

If you want use a different video open test.py and change the parameter video_path of the function real_time_object_detector with the path of your video. Then, launch the following command from the main directory of this repository:

python src/test.py

Download weights

This project use three networks:

  • Faster-RCNN-ResNet50 for object detection
  • EDSR for super resolution
  • ALPRNet for string plate recognition

From this link you can download three folders with all weights needed. Please create a folder named weights in the main directory of this repository and put all three folders downloaded into it. The directory tree must be:

EyeRoad
└── src
│   └── ...
└── weights
    ├── alpr_weights
    ├── detector_weights
    └── edsr_weights