Using deep networks for drone detection

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Date

2017-09-01

Author

Aker, Cemal
Kalkan, Sinan

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Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time.

Subject Keywords

Drones, Birds, Feature extraction, Object detection, Training, Computer architecture

URI

https://hdl.handle.net/11511/42832

DOI

https://doi.org/10.1109/avss.2017.8078539

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

C. Aker and S. Kalkan, “Using deep networks for drone detection,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42832.