A Lightweight ATTENTION- GUIDED BIFPN YOLO11 model FOR DRONE DETECTION

Abstract

With the fast increase in the number of applications that UAVs perform in areas of surveillance, security, disaster management, and commercial operations, there has developed a very significant requirement for efficient and accurate real-time drone detection systems. Nevertheless, the process of detecting UAVs poses great challenges owing to factors such as small object size in image frames, changes in illumination levels, complexity of scenes and motion variations. While YOLO-based deep learning methods achieve real-time capabilities, they still struggle in detecting small UAVs and coping with complex environments. This thesis presents a state-of-the-art UAV object detector model based on YOLO11. This architecture is enhanced using two additional models – the Convolutional Block Attention Module (CBAM) and the Bi - directional Feature Pyramid Network (BiFPN). The purpose is to increase the ability of this model to detect smaller objects while maintaining efficiency and fast operation. The first enhancement allows improving the detection of key features of the drone objects both spatially and through channel attention. The other modification makes it possible for the model to improve its multi-level feature representations since it facilitates two-way fusion of the features from different pyramid levels, which helps in localization of the drones at different levels. The models were trained and tested using the open - source dataset YOLO Drone Detection Dataset, available at Kaggle, that contains numerous examples of indoor, outdoor, distant, and outline drones having 347 samples for validation along with 369 labeled drones. Both YOLO11n and YOLO11s models were experimented with extensively using the same training set-up. For performance evaluation, the standard object detection parameters such as Precision , Recall , mAP@50 , mAP@50-95 , F1-Score and Frames Per Second (FPS) were used. The experimental results show that YOLO11s + CBAM + BiFPN obtained a Precision of 94.8%, Recall of 93.7%, mAP@50 of 96.1%, mAP@50-95 of 71.8% and F1-Score of 94.2%. These correspond to gains of +2.4%, +5.6%, +2.5%, and +3.3% respectively compared to the plain YOLO11s baseline, while still running in real-time at 75 FPS on an NVIDIA Tesla T4 GPU. Further analysis of qualitative confidence demonstrated that YOLO11s + CBAM + BiFPN had an average detection confidence of 16.4% higher than YOLO11n + CBAM + BiFPN on the same input frames, indicating the benefit of deeper feature extraction for small drone targets. v Collectively, the results indicate that the combination of lightweight attention modules and advanced multi - scale features and fusion can significantly improve the accuracy and robustness of UAV detection. The devised approach exhibited strong robustness under difficult circumstances, including extremely small and faraway drones, reflection, occlusions, and contrastless images. This is a viable and scalable solution to many practical problems such as drone tracking and surveillance.