IoT-based automatic deep learning model generation and the application on Empty-dish Recycling Robots

As a modern computer vision technology, deep learning (DL) revolutionizes our lives and reshapes our world with its high performance. Usually, to accomplish each task, researchers need to collect the dataset and modify the basic DL model to optimize the model. In addition, collecting the dataset is time-consuming, and building separate Artificial Intelligence (AI) models for each task is significantly ineffective Therefore, AI research faces challenges in realizing effective data collection and DL model generation. To solve these problems, we design an Internet of Things (IoT)-based automatic DL model generation strategy which also includes an IoT-based data collection method. In detail, we design an Android application that captures images and sends them to a cloud server to create a dataset. Additionally, we apply a two-level checking function to filter out anomalous data and ensure the dataset's correctness. Next, the cloud server uses the dataset to train and generate a DL model automatically. We have prepared the stateof-the-art DL components in the cloud server and propose an automatic DL model creation process for model generation. We have applied the proposal to Empty-dish Recycling Robots for demonstration and evaluation purposes to easily understand the proposal and measure the performance. The experimental results show that the system successfully collects the dataset and automatically generates the DL model. Furthermore, the checking function deployed on Android devices requires only 0.84 MB and achieves 99.86% accuracy. During training, the time spent on each automatic DL model generation is evidently decreased by about 9.00% to 28.00%.