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Data labeling is a time intensive process. As such, many data scientists usevarious tools to aid in the data generation and labeling process. While thesetools help automate labeling, many still require user interaction throughoutthe process. Additionally, most target only a few network frameworks. Anyresearchers exploring multiple frameworks must find additional tools orwriteconversion scripts. This paper presents an automated tool for generatingsynthetic data in arbitrary network formats. It uses Robot Operating System(ROS) and Gazebo, which are common tools in the robotics community. Through ROSparadigms, it allows extensive user customization of the simulation environmentand data generation process. Additionally, a plugin-like framework allows thedevelopment of arbitrary data format writers without the need to change themain body of code. Using this tool, the authors were able to generate anarbitrarily large image dataset for three unique training formats usingapproximately 15 min of user setup time and a variable amount of hands-off runtime, depending on the dataset size. The source code for this data generationtool is available at https://github.com/Navy-RISE-Lab/nn_data_collection

Automatic Generation of Machine Learning Synthetic Data Using ROS