Transformer-Based Multi-Task Segmentation Framework for Dead Broiler Identification

Efficient monitoring of large-scale poultry farms requires the timely identification of dead broilers, as delays can accelerate disease transmission, leading to significant economic loss. Nevertheless, manual inspection remains the dominant practice, resulting in a labor-intensive, inconsistent, and poorly scalable workflow. Although recent advances in computer vision have introduced automated alternatives, most existing approaches face difficulties in crowded settings where live and dead broilers share similar visual patterns, and occlusions frequently occur. To address these problems, we propose a transformer-based multi-task segmentation framework designed to operate reliably in visually complex farm environments. The model constructs a unified feature representation that supports precise segmentation of dead broilers, while an auxiliary dead broiler counting task contributes additional supervisory features that enhance segmentation performance across diverse scene configurations. Experimental evaluations indicate that the proposed method yields accurate and stable segmentation results under various farm conditions, including densely populated and visually intricate scenes. Moreover, its overall segmentation accuracy consistently surpasses that of existing approaches, demonstrating the effectiveness of integrating transformer-based global modeling with the auxiliary regression objective.