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

arXiv: https://arxiv.org/abs/2509.14012 · HTML: https://mlsanity.com/paper/2509.14012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 75 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Stable Architectures for Deep Neural Networks](https://mlsanity.com/paper/1705.03341/) (2017)
- [Decoupling Features in Hierarchical Propagation for Video Object Segmentation](https://mlsanity.com/paper/2210.09782/) (2022)
- [Vision-based Anti-UAV Detection and Tracking](https://mlsanity.com/paper/2205.10851/) (2022)
- [Adaptive wavelet distillation from neural networks through interpretations](https://mlsanity.com/paper/2107.09145/) (2021)
- [Designing Network Design Strategies Through Gradient Path Analysis](https://mlsanity.com/paper/2211.04800/) (2022)
- [Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection](https://mlsanity.com/paper/2408.15020/) (2024)
- [A Deep Learning Approach to Drone Monitoring](https://mlsanity.com/paper/1712.00863/) (2017)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases](https://mlsanity.com/paper/2401.00926/) (2024)
- [Frequency-Guided Spatial Adaptation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.12421/) (2024)
- [Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities](https://mlsanity.com/paper/2207.01927/) (2022)
- [PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing](https://mlsanity.com/paper/2207.14131/) (2022)
- [SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection](https://mlsanity.com/paper/2411.05633/) (2024)
- [Slim-neck by GSConv: A lightweight-design for real-time detector architectures](https://mlsanity.com/paper/2206.02424/) (2022)
- [CamSAM2: Segment Anything Accurately in Camouflaged Videos](https://mlsanity.com/paper/2503.19730/) (2025)
- [YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection](https://mlsanity.com/paper/2406.11641/) (2024)
- [Long Range Object-Level Monocular Depth Estimation for UAVs](https://mlsanity.com/paper/2302.08943/) (2023)
- [Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer](https://mlsanity.com/paper/2403.01736/) (2024)

## Built on by (most cited first, 2 of 2)

- [Detector-Augmented SAMURAI for Long-Duration Drone Tracking](https://mlsanity.com/paper/2601.04798/) (2026)
- [Beyond Clear Skies: Synthetic Seasonal and Weather Variations for Real-World Drone Detection](https://mlsanity.com/paper/2608.16191/) (2026)

## Abstract (arXiv)

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.