Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Object detection models often struggle with class imbalance, where rare
categories appear significantly less frequently than common ones. Existing
sampling-based rebalancing strategies, such as Repeat Factor Sampling (RFS) and
Instance-Aware Repeat Factor Sampling (IRFS), mitigate this issue by adjusting
sample frequencies based on image and instance counts. However, these methods
are based on linear adjustments, which limit their effectiveness in long-tailed
distributions. This work introduces Exponentially Weighted Instance-Aware
Repeat Factor Sampling (E-IRFS), an extension of IRFS that applies exponential
scaling to better differentiate between rare and frequent classes. E-IRFS
adjusts sampling probabilities using an exponential function applied to the
geometric mean of image and instance frequencies, ensuring a more adaptive
rebalancing strategy. We evaluate E-IRFS on a dataset derived from the
Fireman-UAV-RGBT Dataset and four additional public datasets, using YOLOv11
object detection models to identify fire, smoke, people and lakes in emergency
scenarios. The results show that E-IRFS improves detection performance by 22\%
over the baseline and outperforms RFS and IRFS, particularly for rare
categories. The analysis also highlights that E-IRFS has a stronger effect on
lightweight models with limited capacity, as these models rely more on data
sampling strategies to address class imbalance. The findings demonstrate that
E-IRFS improves rare object detection in resource-constrained environments,
making it a suitable solution for real-time applications such as UAV-based
emergency monitoring.
