Deep Learning Empowers Smart Animal Husbandry: Precise Localization and Image Segmentation of Specific Parts of Sika Deer

Caocan Zhu, Jinfan Wei, Tonghe Liu, He Gong, Juanjuan Fan () and Tianli Hu ()
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Caocan Zhu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jinfan Wei: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Tonghe Liu: Jilin Province Agricultural Mechanization Management Center, Changchun 130118, China
He Gong: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Juanjuan Fan: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Tianli Hu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 16, 1-24

Abstract: In precision livestock farming, synchronous and high-precision instance segmentation of multiple key body parts of sika deer serves as the core visual foundation for achieving automated health monitoring, behavior analysis, and automated antler collection. However, in real-world breeding environments, factors such as lighting changes, severe individual occlusion, pose diversity, and small targets pose severe challenges to the accuracy and robustness of existing segmentation models. To address these challenges, this study proposes an improved model, MPDF-DetSeg, based on YOLO11-seg. The model reconstructs its neck network, and designs the multipath diversion feature fusion pyramid network (MPDFPN). The multipath feature fusion and cross-scale interaction mechanism are used to solve the segmentation ambiguity problem of deer body occlusion and complex illumination. The design depth separable extended residual module (DWEResBlock) improves the ability to express details such as texture in specific parts of sika deer. Moreover, we adopt the MPDIoU loss function based on vertex geometry constraints to optimize the positioning accuracy of tilted targets. In this study, a dataset consisting of 1036 sika deer images was constructed, covering five categories, including antlers, heads (front/side views), and legs (front/rear legs), and used for method validation. Compared with the original YOLO11-seg model, the improved model made significant progress in several indicators: the mAP50 and mAP50-95 under the bounding-box metrics increased by 2.1% and 4.9% respectively; the mAP50 and mAP50-95 under the mask metrics increased by 2.4% and 5.3%, respectively. In addition, in the mIoU index of image segmentation, the model reached 70.1%, showing the superiority of this method in the accurate detection and segmentation of specific parts of sika deer, this provides an effective and robust technical solution for realizing the multidimensional intelligent perception and automated applications of sika deer.

Keywords: automated antler collection; MPDFPN; DWEResBlock; object detection; image segmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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