Evaluating AI Effectiveness in Small and Rare Object Detection Using Controlled Synthetic Data Generation Environments
Document Type
Article
Publication Date
2026
Abstract
The application of Artificial Intelligence (AI) for object detection has seen significant advancements, yet detecting physically small and rare objects remains a challenge due to the limited availability of labeled real-world data. To overcome this challenge, we generated synthetic data using Unreal Engine 5.0 and UnrealGT to augment the training datasets for object detection models. Our approach integrates both synthetic and real-world data, with hand-labeled annotations created using the CVAT tool, allowing models such as YOLOv8, YOLOv11, YoloNAS, and Detectron2 with Slicing Aided Hyper Inference (SAHI) to be trained under various environmental conditions. We explored weather, lighting, and proximity variations to ensure the robustness of the models used and validate our approach during comparative analyzes. The results demonstrate the effectiveness of synthetic data in improving model performance, particularly for rare objects in complex environments. This methodology offers promising applications in surveillance, security, and other real-world use cases where rare object detection is critical. © 2026 IEEE.
Recommended Citation
Spooner, Catherine; Claiborne, Jesse; Delgado, Miriam; Backus, Michael; and Bhattacharya, Sambit, "Evaluating AI Effectiveness in Small and Rare Object Detection Using Controlled Synthetic Data Generation Environments" (2026). College of Health, Science, and Technology. 1210.
https://digitalcommons.uncfsu.edu/college_health_science_technology/1210