SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks
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Abstract (arXiv)
Detecting and localizing poultry is essential for advancing smart poultry farming. Despite the progress of detection-centric methods, challenges persist in free-range settings due to multiscale targets, obstructions, and complex or dynamic backgrounds. To tackle these challenges, we introduce an innovative poultry detection approach named SFN-YOLO that utilizes scale-aware fusion. This approach combines detailed local features with broader global context to improve detection in intricate environments. Furthermore, we have developed a new expansive dataset (M-SCOPE) tailored for varied free-range conditions. Comprehensive experiments demonstrate our model achieves an mAP of 80.7% with just 7.2M parameters, which is 35.1% fewer than the benchmark, while retaining strong generalization capability across different domains. The efficient and real-time detection capabilities of SFN-YOLO support automated smart poultry farming.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 21 Sep 2025.
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References · 8
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
- 2024Mamba YOLO: A Simple Baseline for Object Detection with State Space Modelcited by 19
- 2025FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detectioncited by 13
- 2024CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical Imagerycited by 3
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