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Arxiv
3d
64
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Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios
- Object detection models often struggle with class imbalance, where rare categories appear significantly less frequently than common ones.
- Existing rebalancing strategies for class imbalance, such as Repeat Factor Sampling (RFS) and Instance-Aware Repeat Factor Sampling (IRFS), have limitations 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 rebalance rare and frequent classes in object detection.
- E-IRFS improves detection performance by 22% over the baseline, outperforming RFS and IRFS, especially for rare categories, in resource-constrained environments like UAV-based emergency monitoring.
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