SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks
This addresses poultry detection for smart farming, but it is incremental as it builds on existing YOLO methods with a new fusion approach and dataset.
The paper tackles the problem of detecting poultry in free-range settings with multiscale targets and complex backgrounds by introducing SFN-YOLO, a scale-aware fusion network, achieving an mAP of 80.7% with 7.2M parameters, which is 35.1% fewer than the benchmark.
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. The code and dataset can be accessed at https://github.com/chenjessiee/SFN-YOLO.