RE-FB: An Efficient Vision Model for Feeding Behavior Detection and Group-Level Feeding Activity Assessment in Broilers Housed in Multi-Tier Cages

Automated monitoring of broiler feeding can improve management, but occlusion and scale variation hinder reliable detection in commercial multi-tier cages. This study developed a lightweight vision model for feeding behavior detection (RE-FB) and a feeding activity level (FAL) indicator for group-level assessment. The 2300 annotated images of yellow-feathered broilers were divided 8:1:1 for training, validation, and testing. RE-FB combines reparameterized multi-branch feature extraction, attention, and bidirectional multi-scale fusion to detect feeding birds and segment cages and feed troughs. On the test set, RE-FB-m achieved 89.7% precision, 87.3% recall, 92.1% box mAP@0.5, and 99.6% mask mAP@0.5, with 8.1 million parameters and a reported model inference speed of 158 FPS on the test platform. RE-FB-m achieved the highest box mAP@0.5 for feeding behavior detection among the evaluated Faster R-CNN, RT-DETR, and YOLO models. RE-FB was then applied to videos of four fixed groups monitored from 11 to 17 days of age. FAL was defined as the time-averaged proportion of the total birds in each monitored group detected as feeding, expressed as a percentage. Age-specific mean FAL values across the four groups ranged from 7.95% to 8.74%, with no significant age effect detected (p = 0.175). After controlling for group effects, the association between FAL and same-day weight gain was not statistically significant (partial r = −0.106, p = 0.614). RE-FB accurately and efficiently detects feeding behavior in caged broilers and generates group-level FAL records by aggregating detections over time. This approach provides a technical basis for non-contact monitoring of feeding activity in broiler production and can provide quantitative behavioral information for feeding management.