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SFN-YOLO model enhances poultry detection with scale-aware fusion

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SFN-YOLO, a novel approach for detecting and localizing poultry in free-range farming environments. This method employs scale-aware fusion to integrate local and global features, enhancing detection accuracy amidst complex backgrounds and varied target sizes. The accompanying M-SCOPE dataset and SFN-YOLO model demonstrate strong performance, achieving 80.7% mAP with significantly fewer parameters than existing benchmarks, enabling real-time application in smart farming systems. AI

IMPACT Improves efficiency and automation in agriculture through enhanced object detection capabilities.

RANK_REASON Publication of an academic paper detailing a new computer vision model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SFN-YOLO model enhances poultry detection with scale-aware fusion

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Publication of an academic paper detailing a new computer vision model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jie Chen, Yuhong Feng, Tao Dai, Hao Wang, Hongtao Chen, Zhaoxi He, Mingzhe Liu, Jiancong Bai ·

    SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks

    arXiv:2509.17086v2 Announce Type: replace 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…

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