Predominant methods for image-based drone detection frequently rely on
employing generic object detection algorithms like YOLOv5. While proficient in
identifying drones against homogeneous backgrounds, these algorithms often
struggle in complex, highly textured environments. In such scenarios, drones
seamlessly integrate into the background, creating camouflage effects that
adversely affect the detection quality. To address this issue, we introduce a
novel deep learning architecture called YOLO-FEDER FusionNet. Unlike
conventional approaches, YOLO-FEDER FusionNet combines generic object detection
methods with the specialized strength of camouflage object detection techniques
to enhance drone detection capabilities. Comprehensive evaluations of
YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate
substantial improvements in both reducing missed detections and false alarms.

通过将通用目标检测算法与专门的伪装物体检测技术相结合，我们引入了一种名为 YOLO-FEDER FusionNet 的创新深度学习架构，提高了无人机检测能力，通过全面评估证明了该模型的高效性，并在减少漏检和误报方面取得了显著的改进。