Investigation of UAV Detection in Images With Complex Backgrounds and Rainy Artifacts

Adnan Munir, Abdul Jabbar Siddiqui, Saeed Anwar; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 221-230

To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and deep learning approaches are developing areas of research. 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 the task of UAV detection. However, UAV detection in images with complex backgrounds and weather artifacts such as rain has not been well studied. In order to address this issue, two training datasets were developed. The first training dataset has the sky as its background and is referred to as 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 one containing images with three types of 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 the task of 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 the task of UAV detection under challenging conditions and pave the way to develop more robust UAV detection methods. The codes and datasets will be released later.