Overview / Usage

This project demonstrates the use of TensorFlow Object Detection API to automatically number plates (Indian) from vehicles.

Dataset used: https://www.kaggle.com/dataturks/vehicle-number-plate-detection

To kick-start the model training process, I followed the steps from TensorFlow Object Detection API's official documentation: https://github.com/tensorflow/models/tree/master/research/object_detection. I used SSD_MobileNet_V1 architecture which was pretrained on the COCO dataset.

The main challenge was to prepare the data for TensorFlow Object Detection API. The dataset comes in YOLO dataset format, so a decent amount of effort has been put to prepare the dataset compatible for using with TensorFlow Object Detection API.

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Methodology / Approach

Dataset used: https://www.kaggle.com/dataturks/vehicle-number-plate-detection

To kick-start the model training process, I followed the steps from TensorFlow Object Detection API's official documentation: https://github.com/tensorflow/models/tree/master/research/object_detection. I used SSD_MobileNet_V1 architecture which was pretrained on the COCO dataset.

The main challenge was to prepare the data for TensorFlow Object Detection API. The dataset comes in YOLO dataset format, so a decent amount of effort has been put to prepare the dataset compatible for using with TensorFlow Object Detection API. After the model was trained I used OpenVINO to further optimize the network so that I could run inference on my Neural Compute Stick.

Technologies Used

• TensorFlow Object Detection API

• Google Cloud Platform

• OpenVINO

Repository

https://github.com/sayakpaul/Vehicle-Number-Plate-Detection

Collaborators