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

• URL: http://arxiv.org/abs/2509.14012v1
• Date: Wed, 17 Sep 2025 14:21:00 GMT
• Title: Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
• Authors: Tamara R. Lenhard, Andreas Weinmann, Tobias Koch,
• Abstract summary: Drone detection in visually complex environments is challenging due to background clutter, small object scale, and camouflage effects.<n>This work presents an enhanced iteration of YOLO-FEDER FusionNet -- a detection framework that integrates generic object detection with camouflage object detection techniques.
• Score: 0.981328290471248
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• YOLO-DS: Fine-Grained Feature Decoupling via Dual-Statistic Synergy Operator for Object Detection [55.58092342624062]
  We propose YOLO-DS, a framework built around a novel Dual-Statistic Synergy Operator (DSO)<n>YOLO-DS decouples object features by jointly modeling the channel-wise mean and the peak-to-mean difference.<n>On the MS-COCO benchmark, YOLO-DS consistently outperforms YOLOv8 across five model scales.
  arXiv  Detail & Related papers  (2026-01-26T05:50:32Z)
• Semantic-Guided Natural Language and Visual Fusion for Cross-Modal Interaction Based on Tiny Object Detection [6.895355763564631]
  This paper introduces a cutting-edge approach to cross-modal interaction for tiny object detection by combining semantic-guided natural language processing with advanced visual recognition backbones.<n>The proposed method integrates the BERT language model with the CNN-based Parallel Residual Bi-Fusion Feature Pyramid Network.<n>By employing lemmatization and fine-tuning techniques, the system aligns semantic cues from textual inputs with visual features, enhancing detection precision for small and complex objects.
  arXiv  Detail & Related papers  (2025-11-07T18:38:00Z)
• YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception [58.06752127687312]
  We propose YOLOv13, an accurate and lightweight object detector.<n>We propose a Hypergraph-based Adaptive Correlation Enhancement (HyperACE) mechanism.<n>We also propose a Full-Pipeline Aggregation-and-Distribution (FullPAD) paradigm.
  arXiv  Detail & Related papers  (2025-06-21T15:15:03Z)
• MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection [12.838872442435527]
  Small object detection in UAV imagery is crucial for applications such as search-and-rescue, traffic monitoring, and environmental surveillance.<n>Existing multi-scale fusion methods help, but add computational burden and blur fine details.<n>We propose a unified fusion framework that tightly couples global context with local detail to boost detection performance.
  arXiv  Detail & Related papers  (2025-06-15T02:54:25Z)
• YOLO-SPCI: Enhancing Remote Sensing Object Detection via Selective-Perspective-Class Integration [1.2815904071470707]
  YOLO-S PCI is an attention-enhanced detection framework that introduces a lightweight Selective-Perspective-Class Integration module.<n>YOLO-S PCI achieves superior performance compared to state-of-the-art detectors.
  arXiv  Detail & Related papers  (2025-05-27T16:00:34Z)
• Efficient Feature Fusion for UAV Object Detection [9.632727117779178]
  Small objects, in particular, occupy small portions of images, making their accurate detection difficult.<n>Existing multi-scale feature fusion methods address these challenges by aggregating features across different resolutions.<n>We propose a novel feature fusion framework specifically designed for UAV object detection tasks.
  arXiv  Detail & Related papers  (2025-01-29T20:39:16Z)
• What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector [0.0]
  This study focuses on the YOLOv9 object detection model, focusing on its architectural innovations, training methodologies, and performance improvements. Key advancements, such as the Generalized Efficient Layer Aggregation Network GELAN and Programmable Gradient Information PGI, significantly enhance feature extraction and gradient flow. This paper provides the first in depth exploration of YOLOv9s internal features and their real world applicability, establishing it as a state of the art solution for real time object detection.
  arXiv  Detail & Related papers  (2024-09-12T07:46:58Z)
• YOLO-World: Real-Time Open-Vocabulary Object Detection [87.08732047660058]
  We introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed.
  arXiv  Detail & Related papers  (2024-01-30T18:59:38Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)
• EDN: Salient Object Detection via Extremely-Downsampled Network [66.38046176176017]
  We introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image. Experiments demonstrate that EDN achieves sArt performance with real-time speed.
  arXiv  Detail & Related papers  (2020-12-24T04:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.