Method for detecting dead caged laying ducks based on infrared thermal imaging

To accurately and efficiently detect dead caged laying ducks, thereby reducing reliance on manual inspection, this study proposes a method that integrates infrared thermography with deep learning technology. A lightweight object detection algorithm is developed, utilizing YOLO v8n as the baseline model. The backbone network is replaced with StarNet, which is based on “Star Operate”. Additionally, the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module, and it is inserted into the Neck structure of the baseline model. Lightweight module L-SPPF replaces the SPPF module in the baseline model to enhance feature augmentation. Furthermore, a lightweight shared convolutional detection head, termed SCSB-Head, is introduced to reduce computational complexity. These improvements collectively form a lightweight object detection algorithm named SLSS-YOLO. Experimental results show that SLSS-YOLO achieves mAP@50%-95%, precision, and recall scores of 80.50%, 99.44%, and 98.46%, respectively. Compared to the baseline model, these metrics improve by 1%, 1.98%, and 0.26%, respectively. In terms of model size and detection speed, SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs, achieving an FPS rate of 134.9 f/s. This represents a reduction of 52.16% and 43.90% in parameters and FLOPs, respectively, while increasing FPS by 5.4 f/s compared to the baseline model. Moreover, an object tracking model is constructed using SLSS-YOLO and Hybrid-SORT. Tracking tests demonstrate that Hybrid-SORT achieves zero ID-Switches, with a detection speed of 10.9 ms/f. It outperforms Bot-SORT, ByteTrack, Deep OC-SORT, and OC-SORT in terms of tracking performance. Therefore, the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments, providing a reference for automated inspection in caged duck farms.