For Precision Animal Husbandry: Precise Detection of Specific Body Parts of Sika Deer Based on Improved YOLO11

Abstract

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.