Dual-Modal Chicken Mortality Detection Using Dynamic Hybrid Convolution-Based Feature Fusion

In large-scale caged broiler farms, daily inspection of dead broilers is essential for flock health management and disease prevention. To address the significant performance degradation of existing methods under challenging conditions such as poor lighting, severe occlusion, and complex backgrounds, this paper proposes a dual-modal dynamic hybrid convolutional feature fusion method for dead bird detection based on an improved YOLO11 framework, termed YOLO11-DualDynConv-FF. First, a dual-modal fusion network architecture was developed to combine RGB and infrared (IR) images, enabling the model to simultaneously process both modalities. By integrating complementary information from RGB and IR data, the proposed method significantly improved detection accuracy and efficiency under low-light conditions. Second, a dynamic hybrid convolution feature fusion module was designed to merge multi-scale feature maps with contextual information, allowing the network to capture fine-grained details and adapt better to complex farming environments. In addition, an occlusion-aware module was introduced to specifically address the physical occlusion challenges prevalent in crowded cage settings. Comparative experiments and ablation studies involving multiple object detection networks were conducted to evaluate the proposed method. The results show that the improved YOLO11 model achieves superior performance, with precision, recall, F1-score, and mAP@0.5 reaching 92.6%, 79.0%, 0.85, and 80.1%, respectively. These results represent improvements of 2.0%, 5.0%, 0.17, and 12.1%, respectively, over the original YOLO11 model. Compared with existing approaches, the proposed model is better suited to complex real-world poultry farming environments and achieves higher detection accuracy, providing a valuable reference for intelligent monitoring in caged poultry farming.