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

Yu Yan, Qiaohua Wang, Weiguo Lin, Shucai Wang, Yue Gu, Yifan Heng

Abstract


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-SORTin terms of tracking performance. Therefore, the proposed thermal infrared detection method can effectively identify and trackdead ducks in complex cage environments, providing a reference for automated inspection in caged duck farms.
Keywords: caged laying duck, object detection algorithm, YOLO, infrared thermal imaging, dead poultry
DOI: 10.25165/j.ijabe.20241706.8314

Citation: Yan Y, Wang Q H, Lin W G, Wang S C, Gu Y, Heng Y F. Method for detecting dead caged laying ducks based on infrared thermal imaging. Int J Agric & Biol Eng, 2024; 17(6): 101–110.

Keywords


caged laying duck, object detection algorithm, YOLO, infrared thermal imaging, dead poultry

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References


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