用多尺度融合提升散养禽类检测精度
SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks
- 融合局部细节与全局上下文信息增强检测能力
- mAP达80.7%，参数量比基准少35.1%
- 适合智能养殖场景的实时高效检测
精准检测与定位禽类对推动智慧养殖至关重要。尽管检测方法已有进展，但在散养环境中仍面临目标多尺度、遮挡及复杂动态背景等挑战。为此，本文提出SFN-YOLO检测方法，采用多尺度感知融合机制，结合局部细节与全局上下文信息，提升复杂环境下的检测性能。同时，构建了针对多样化散养条件的新数据集M-SCOPE。大量实验表明，该模型仅需7.2M参数即达到80.7%的mAP，较基准减少35.1%参数量，且在不同域间保持强泛化能力。SFN-YOLO具备高效实时检测能力，可支持自动化智慧养殖。
原文摘要 · Abstract (English)
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.
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