Enhancing Rare Object Detection in AI: Leveraging Synthetic Data for Improved Model Training
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
Artificial Intelligence (AI) models for object detection face challenges when tasked with identifying rare objects due to the scarcity of data for these items. This scarcity often leads to mediocre performance, as the model lacks sufficient training examples for these specific objects. Acquiring real-world data for such rare objects can also be a challenging and time-consuming process. To address this issue, we developed a method aimed at improving object detection for rare objects. Our method involves the generation of synthetic data, which is subsequently utilized to train AI models. By incorporating synthetic data into the training process, we augment the available dataset, enabling AI models to better recognize and classify rare objects. The synthetic data production methods of this work can be used to build AI applications in areas such as physical security and surveillance, and self-driving vehicles which need to detect rare objects on roads to navigate safely.
Recommended Citation
Claiborne, Jesse; Brown, Tivon; Rodriguez, Paul; and Bhattacharya, Sambit, "Enhancing Rare Object Detection in AI: Leveraging Synthetic Data for Improved Model Training" (2024). College of Health, Science, and Technology. 186.
https://digitalcommons.uncfsu.edu/college_health_science_technology/186