草食家畜Issue(2)：64-70,7.DOI:10.16863/j.cnki.1003-6377.2026.02.007
基于改进YOLOv11的羊只检测方法研究
Research on a Sheep Detection Method Based on Improved YOLOv11 Module
摘要
Abstract
[Objective]With the continuous advancement of deep learning technologies,the application of object detection in the field of animal husbandry has garnered increasing attention.For the purpose of enhancing the accuracy and robustness of sheep detection,an improved YOLOv11-based object detection method for sheep was proposed,aiming to overcome the limitations of traditional detection models in terms of computational efficiency and precision.[Methods]The proposed approach integrated a DynamicConv module in place of conventional downsampling structure,optimizing parameter efficiency without increasing the computational complexity(FLOPs).Additionally,adaptive convolution technique was employed to dynamically adjust input features,significantly improving multi-scale object detection capability.The CBAM attention mechanism was further incorporated to enhance the model's sensitivity to key features.[Results]As a result,the improved model achieved increases of 1.1%,2.8%,and 0.3%in Precision,Recall,and mAP50,respectively,compared to the baseline.[Conclusion]The proposed method effectively enhanced the performance of object detection of sheep.关键词
羊只检测/YOLOv11/目标检测/动态卷积/注意力机制Key words
sheep detection/YOLOv11/object detection/dynamic convolution/attention mechanism分类
农业科技引用本文复制引用
万玉辉,陈新文,左晓佳,赛迪古丽·赛买提,叶尔兰·谢尔毛拉,靳晟..基于改进YOLOv11的羊只检测方法研究[J].草食家畜,2026,(2):64-70,7.基金项目
新疆维吾尔自治区重点研发计划项目"数字畜牧业关键技术研究与开发"(2023B02013-2) （2023B02013-2）
