WRN-YOLO: An Improved YOLO for Drone Detection using Wide ResNet

Yi Jie Wong, Wingates Voon, Mau-Luen Tham, Ban-Hoe Kwan, Yoong Choon Chang, Yan Chai Hum

Abstract: The widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones has introduced significant security and privacy challenges, particularly concerning unauthorized drone activities near sensitive areas. To address these concerns, we propose a novel drone detection model, WRN-YOLO, which integrates the Wide Residual Network (WRN) architecture with the You Only Look Once (YOLO) object detection framework. This integration enhances feature extraction capabilities, leading to improved detection accuracy. Through comprehensive ablation studies, we have identified the optimal YOLO variant that synergizes with our backbone modifications, ensuring superior performance in diverse scenarios. Recognizing the complexities of real-world environments, we have also developed a synthetic dataset designed to train our WRN-YOLO. This dataset encompasses a variety of challenging conditions, including intricate backgrounds and the presence of confounding elements, to robustly assess the model's efficacy. Experimental results demonstrate that our method significantly outperforms existing models in accurately detecting drones amidst complex scenes, offering a promising solution for real-time UAV threat mitigation. The proposed approach ranked Top 3 in the 8th WOSDETC Drone-vs-Bird Detection Challenge. Our source code and synthetic dataset are publicly available at https://github.com/yjwong1999/IJCNN2025-DvB.

External IDs:dblp:conf/ijcnn/WongVTKCH25