Combining Public and On-Farm Datasets for Deep Learning-Based Broiler Detection and Counting in a Commercial Broiler House

The high-density rearing environment in the domestic broiler industry causes birds to overlap and obscure one another, which is a major factor leading to reduced performance in object-recognition-based monitoring; consequently, the need for video-based automated analysis technology is growing. Accordingly, this study established an integrated video monitoring pipeline—ranging from object detection to individual counting—by combining public datasets with on-site CCTV datasets to ensure field adaptability, and by integrating heterogeneous detector fusion with multi-object tracking. A training dataset was constructed by combining the AI-Hub Intelligent Smart Poultry House Broiler Video Dataset with CCTV footage collected from a commercial broiler farm. The convolutional neural network-based YOLOv8m and Transformer-based RT-DETR-L were trained independently, and their predictions were subsequently fused using Weighted Boxes Fusion (WBF). ByteTrack was applied for tracking to maintain object identity across frames, and a virtual line crossing counting algorithm was implemented to automatically determine the points where tracking trajectories intersect with virtual lines set within the video, thereby calculating the cumulative number of objects by direction. The experimental results showed that, after training with field data, the model achieved a mean average precision at an intersection over union (IoU) threshold of 0.5 (mAP@0.5) of 0.9880 based on YOLOv8m, and the WBF fusion improved the mAP@0.5 by 26.4% compared to the best single model among the pairs trained solely on public data. The YOLOv8m model trained using combined data achieved an average absolute error of 6.33 birds and an accuracy of 97.60% in frame-level object counting, and recorded an F1-score of 0.856 in virtual line crossing counting. This study confirmed that combined training using public and field data is effective in mitigating domain gaps and that frame-level object counting can be utilized as an indicator for flock monitoring.