Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments

Abstract

Most computer vision applications that use deep learning on constrained device come from the Internet of Things (IoT) or robotics fields, where low-quality cameras are used to capture input images in real time. Since most pretrained models typically undergo training on high-quality image datasets, the low-quality, noisy, or blurry images captured with these resource-constrained devices could possibly have a negative impact on the models' performance. To determine if model performance is impacted by training models using low-quality data, a secondary image dataset named MOD-2022 was prepared for object detection and tracking tasks using an exploratory data preparation methodology. This dataset was primarily designed to include a wider range of classes with an adequate number of images per class while being free of errors due to inaccurate labelling or annotations. Additionally, a training approach is also proposed to support the model's training when the dataset is considerably large. A VGG16-SSD model was trained with this approach on the prepared dataset and deployed on a Raspberry Pi and it showed that this approach is very useful in developing models for resource-constrained applications. Furthermore, this data preparation approach can be extended to prepare numerous other datasets required for training models designed to be deployed on constrained devices similar to the Raspberry Pi.