** Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts ** 

This is the code for UAV detection with clean and rainy artifacts. We have choosen four SOTA object detctors including Faster-RCNN, RetinaNet, YOLOv5, and YOLOv8.
The weight file for YOLOv5, YOLOv8, Faster-RCNN and RetinaNet can be downloaded from below given google drive links:

YOLOv5: https://drive.google.com/file/d/1gkNbyStZ6P4h4cNn146-x8hhJHxhmOyP/view?usp=sharing

YOLOv8: https://drive.google.com/file/d/1PjOGlzB87Dtmxb70wb1n3w2x3YZQevEG/view?usp=sharing

Faster-RCNN: https://drive.google.com/file/d/1laYF7cIty_65na-uYQIbTVxXi83ewj61/view?usp=sharing

RetinaNet: https://drive.google.com/file/d/1zT6kBEEBPtpU1EbgbYkesF35FuKI9UhA/view?usp=sharing

The complex background dataset (CBD) can be downloaded from this link 

"https://drive.google.com/file/d/1-BmnQe9LllS7EA4NhGGj-2f7MKZjfnR-/view?usp=sharing"


If you find the repository useful please cite the following paper:

@InProceddings {
author = { Adnan Munir, Abdul Jabbar Siddiqui, and Saeed Anwar},
title = {Investigation-of-UAV-Detection-in-Images-with-Complex-Backgrounds-and-Rainy-Artifacts},
booktitle = {Proceeding of the IEEE/CVF winter Conference on Application of Computer Vision},
month = {November},
year = {2023},
}