YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection

TL;DR

YOLO-FEDER FusionNet is a new deep learning model that improves drone detection in complex environments by combining generic object detection with camouflage detection techniques, reducing errors.

Contribution

It introduces a novel architecture that integrates camouflage detection with YOLO-based methods for enhanced drone detection in challenging scenes.

Findings

Significant reduction in missed detections.

Lower false alarm rates.

Improved detection accuracy in textured backgrounds.

Abstract

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.