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.

翻译：基于图像的无人机检测主流方法通常依赖于采用通用目标检测算法，如YOLOv5。这些算法虽然在均匀背景下能有效识别无人机，但在复杂、纹理丰富的环境中往往表现不佳。在此类场景中，无人机与背景无缝融合，产生伪装效应，从而对检测质量造成不利影响。为解决这一问题，我们提出了一种名为YOLO-FEDER FusionNet的新型深度学习架构。与常规方法不同，YOLO-FEDER FusionNet将通用目标检测方法与伪装目标检测技术的专业优势相结合，以增强无人机检测能力。对YOLO-FEDER FusionNet的全面评估表明，所提模型具有高效性，并在减少漏检和误报两方面均展现出显著改进。