Track Boosting and Synthetic Data Aided Drone Detection

• URL: http://arxiv.org/abs/2111.12389v1
• Date: Wed, 24 Nov 2021 10:16:27 GMT
• Title: Track Boosting and Synthetic Data Aided Drone Detection
• Authors: Fatih Cagatay Akyon, Ogulcan Eryuksel, Kamil Anil Ozfuttu, Sinan Onur Altinuc
• Abstract summary: Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data. Our results indicate that augmenting the real data with an optimal subset of synthetic data can increase the performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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