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arXiv 2111.123892022-05-19cs.CVcs.LG

Track Boosting and Synthetic Data Aided Drone Detection

OBSS AI（OBSS人工智能）；OBSS Technology（OBSS科技）

Fatih Cagatay Akyon, Ogulcan Eryuksel, Kamil Anil Ozfuttu, Sinan Onur Altinuc

备注：: Published at AVSS 2021
发表信息：: 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
DOI：: 10.1109/AVSS52988.2021.9663759

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This is the paper for the first place winning solution of the Drone vs. Bird Challenge, organized by AVSS 2021. As the usage of drones increases with lowered costs and improved drone technology, drone detection emerges as a vital object detection task. However, detecting distant drones under unfavorable conditions, namely weak contrast, long-range, low visibility, requires effective algorithms. 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. Our results indicate that augmenting the real data with an optimal subset of synthetic data can increase the performance. Moreover, temporal information gathered by object tracking methods can increase performance further.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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