Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by $-50.62$ points in torrential rain conditions, by $-52.40$ points in high noise conditions, and by $-77.0$ points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained $+35.4$, $+39.3$, and $+44.9$ points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. The code and datasets could be found at: https://github.com/AdnanMunir338/YOLO-RAW.

A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.