How to Train and Deploy a License Plate Detector to the Luxonis OAK

This blog post provides a comprehensive guide on training and deploying a custom license plate detection model to Luxonis OAK devices using Roboflow and DepthAI. It outlines the process of gathering a dataset of annotated license plate images, leveraging Roboflow for data processing and augmentation, and using Google Colab with TensorFlow for model training. The tutorial emphasizes the flexibility of the approach, allowing users to adapt the steps for detecting different objects. After training, the model is exported and converted into formats compatible with DepthAI and OpenVino before being deployed to an OAK device. The post concludes by encouraging users to enhance their models through additional data collection and retraining, highlighting active learning as a key strategy for improving model performance in production environments.

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