The breeding of sika deer has significant economic value in China. However, the traditional management methods have problems such as low efficiency, easy triggering of strong stress responses, and damage to animal welfare. Therefore, the development of non-contact, automated, and precise monitoring and management technologies has become an urgent need for the sustainable development of this industry. In response to this demand, this study designed a model MFW-YOLO based on YOLO11, aiming to achieve precise detection of specific body parts of sika deer in a real breeding environment. Improvements include: designing a lightweight and efficient hybrid backbone network, MobileNetV4HybridSmall; The multi-scale fast pyramid pooling module (SPPFMscale) is proposed. The WIoU v3 loss function is used to replace the default loss function. To verify the effectiveness of the method, we constructed a sika deer dataset containing 1025 images, covering five categories. The experimental results show that the improved model performs well. Its mAP50 and MAP50-95 reached 91.9% and 64.5%, respectively. This model also demonstrates outstanding efficiency. The number of parameters is only 62% (5.9 million) of the original model, the computational load is 60% (12.8 GFLOPs) of the original model, and the average inference time is as low as 3.8 ms. This work provides strong algorithmic support for achieving non-contact intelligent monitoring of sika deer, assisting in automated management (deer antler collection and preparation), and improving animal welfare, demonstrating the application potential of deep learning technology in modern precision animal husbandry.

Automatically measuring the Vertebral Heart Scale (VHS) from canine thoracic X-ray images is an effective approach for the early screening and prevention of cardiomegaly. Establishing automated and precise measurement tools is crucial for advancing population-based research on canine cardiac health. In this study, an integrated framework based on an improved U-Net architecture (SWA-UNet) and YOLOv11 is proposed for precise heart segmentation and key point detection to enable automated and accurate VHS calculation. It provides an efficient, objective, and repeatable auxiliary diagnostic tool for clinical practice, significantly enhancing the standardization of cardiac assessment and diagnostic efficiency. The method achieved a mean Intersection over Union of 95.34 % and a Dice coefficient of 98.20 % for heart segmentation. For key point detection, YOLOv11 attained a precision rate of 96.50 % and a recall rate of 95.20 % for the T3-T10 vertebrae, demonstrating high detection accuracy and reliability. Ultimately, the predicted VHS values showed a strong linear association with manual measurements, with a Pearson correlation coefficient of 0.8985. Furthermore, Bland-Altman analysis indicated that the automated system maintains high consistency with expert assessments, characterized by a minimal mean difference of -0.0696 cm. This result fully validates the effectiveness of this method in addressing challenges such as low-contrast images, blurred boundaries, and structural overlaps. As an auxiliary tool for veterinary radiologists, this method demonstrates great potential and can contribute to efficient and standardized assessment of canine heart diseases.

At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision.