Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments

2025-09-17

AI Summary

To address the challenges of detecting small, camouflaged unmanned aerial vehicles (UAVs) against cluttered and low-texture backgrounds in complex visual environments, this paper proposes YOLO-FEDER FusionNet—a novel framework integrating general object detection with camouflage-aware detection paradigms. It introduces a hybrid training dataset comprising large-scale synthetic data augmented with a small real-world sample set, designs a DWD-guided multi-scale FEDER feature fusion mechanism operating at intermediate network layers, and optimizes the YOLO backbone architecture for enhanced robustness. Evaluated on the YOLOv8l backbone, the method reduces false detection rate by 39.1 percentage points and improves mAP@0.5 by 62.8 percentage points over the baseline. These results validate the effectiveness of the proposed feature fusion strategy and the synergistic data-driven training paradigm in challenging UAV detection scenarios.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Drone detection in visually complex environments remains challenging due to background clutter, small object scale, and camouflage effects. While generic object detectors like YOLO exhibit strong performance in low-texture scenes, their effectiveness degrades in cluttered environments with low object-background separability. To address these limitations, this work presents an enhanced iteration of YOLO-FEDER FusionNet -- a detection framework that integrates generic object detection with camouflage object detection techniques. Building upon the original architecture, the proposed iteration introduces systematic advancements in training data composition, feature fusion strategies, and backbone design. Specifically, the training process leverages large-scale, photo-realistic synthetic data, complemented by a small set of real-world samples, to enhance robustness under visually complex conditions. The contribution of intermediate multi-scale FEDER features is systematically evaluated, and detection performance is comprehensively benchmarked across multiple YOLO-based backbone configurations. Empirical results indicate that integrating intermediate FEDER features, in combination with backbone upgrades, contributes to notable performance improvements. In the most promising configuration -- YOLO-FEDER FusionNet with a YOLOv8l backbone and FEDER features derived from the DWD module -- these enhancements lead to a FNR reduction of up to 39.1 percentage points and a mAP increase of up to 62.8 percentage points at an IoU threshold of 0.5, compared to the initial baseline.

Problem

• Optimizing drone detection in complex visual environments with clutter
• Improving object detection under camouflage and low separability conditions
• Enhancing YOLO-FEDER FusionNet performance through architectural advancements

Innovation

• Integrates YOLO with camouflage detection techniques
• Uses synthetic and real data for training
• Evaluates multi-scale features and backbone upgrades