Using deep networks for drone detection

By Cemal Aker, Sinan Kalkan. 2017

201 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Abdulhadi Shoufan
2. Bilal Taha
3. Jia Li

In Computer Vision and Pattern Recognition

1. Shengwei Zhao
2. Abdulhadi Shoufan
3. Kui Fu
4. Bilal Taha
5. Shiming Ge
6. Jia Li
7. Dacheng Tao
8. James Ze Wang

Citations in the corpus

• Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research (2019, methodology): “proposed an extension of an existing CNNmodel namely, YOLO, which is a single shot object detector [30].”
• Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency (2019, background): “By understanding the human attentional behavior to aerial data, the visual saliency models have the ability to automatically detect, locate and mine the most important part of massive visual information and can facilitate subsequent complex drone vision tasks in both speed and accuracy, such as dron”