{"ID":2868159,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17086","arxiv_id":"2509.17086","title":"SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks","abstract":"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.","short_abstract":"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 dete...","url_abs":"https://arxiv.org/abs/2509.17086","url_pdf":"https://arxiv.org/pdf/2509.17086v2","authors":"[\"Jie Chen\",\"Yuhong Feng\",\"Tao Dai\",\"Hao Wang\",\"Hongtao Chen\",\"Zhaoxi He\",\"Mingzhe Liu\",\"Jiancong Bai\"]","published":"2025-09-21T14:03:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}