Real-time detection of dead broilers in high-density poultry houses using high-resolution video and semantic segmentation

This study proposes a dead broiler detection method that combines lightweight semantic segmentation with temporal modeling. In high-density poultry house scenarios, the proposed approach achieves an overall detection accuracy of 88.64% on a mixed test set containing both dead-broiler and non-dead-broiler samples. Unlike previous studies that rely on single-broiler state analysis, the proposed method is designed for practical commercial farming environments and is capable of reliably localizing 0–4 dead broilers among approximately 1,200 broilers within the field of view of a single camera. The proposed broiler segmentation model maintains a favorable balance between segmentation accuracy and computational efficiency, achieving real-time inference at 34.12 frames per second on 1600 × 2880 resolution images. Experimental results demonstrate that the proposed method enables robust and reliable localization of dead broilers in real-world poultry farming environments, highlighting its practical applicability and providing a feasible foundation for future deployment under higher-resolution wide-angle camera systems.