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Found 2 papers, 0 papers with code
Track Boosting and Synthetic Data Aided Drone Detection
Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data using a Kalman-based object tracker to boost detection confidence.
Ranked #1 on
Object Detection
on Drone vs Bird
(using extra training data)
Increasing Data Diversity with Iterative Sampling to Improve Performance
As a part of the Data-Centric AI Competition, we propose a data-centric approach to improve the diversity of the training samples by iterative sampling.
