Development of Open Informal Dataset Affecting Autonomous Driving
This provides a dataset for improving object recognition in self-driving cars, though it appears incremental as it focuses on data collection procedures rather than novel recognition methods.
The researchers developed a dataset collection methodology for autonomous driving object recognition, collecting 100,000 images of pedestrians and road objects, 200,000 images of police and safety personnel, and building datasets with 5,000 image data points across various environmental conditions.
This document is a document that has written procedures and methods for collecting objects and unstructured dynamic data on the road for the development of object recognition technology for self-driving cars, and outlines the methods of collecting data, annotation data, object classifier criteria, and data processing methods. On-road object and unstructured dynamic data were collected in various environments, such as weather, time and traffic conditions, and additional reception calls for police and safety personnel were collected. Finally, 100,000 images of various objects existing on pedestrians and roads, 200,000 images of police and traffic safety personnel, 5,000 images of police and traffic safety personnel, and data sets consisting of 5,000 image data were collected and built.