Investigation of UAV Detection in Images with Complex Backgrounds and
Rainy Artifacts
- URL: http://arxiv.org/abs/2305.16450v2
- Date: Tue, 5 Dec 2023 18:35:18 GMT
- Title: Investigation of UAV Detection in Images with Complex Backgrounds and
Rainy Artifacts
- Authors: Adnan Munir, Abdul Jabbar Siddiqui, Saeed Anwar
- Abstract summary: Vision-based object detection methods have been developed for UAV detection.
UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied.
This work also focuses on benchmarking state-of-the-art object detection models.
- Score: 20.20609511526255
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and
deep learning approaches are evolving research areas. Interest in this problem
has grown due to concerns regarding the possible hazards and misuse of
employing UAVs in many applications. These include potential privacy
violations. To address the concerns, vision-based object detection methods have
been developed for UAV detection. However, UAV detection in images with complex
backgrounds and weather artifacts like rain has yet to be reasonably studied.
Hence, for this purpose, we prepared two training datasets. The first dataset
has the sky as its background and is called the Sky Background Dataset (SBD).
The second training dataset has more complex scenes (with diverse backgrounds)
and is named the Complex Background Dataset (CBD). Additionally, two test sets
were prepared: one containing clear images and the other with images with three
rain artifacts, named the Rainy Test Set (RTS). This work also focuses on
benchmarking state-of-the-art object detection models, and to the best of our
knowledge, it is the first to investigate the performance of recent and popular
vision-based object detection methods for UAV detection under challenging
conditions such as complex backgrounds, varying UAV sizes, and low-to-heavy
rainy conditions. The findings presented in the paper shall help provide
insights concerning the performance of the selected models for UAV detection
under challenging conditions and pave the way to develop more robust UAV
detection methods. The codes and datasets are available at:
https://github.com/AdnanMunir294/UAVD-CBRA.
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