华南农业大学学报2026，Vol.47Issue(4)：698-709,12.DOI:10.7671/j.issn.1001-411X.202601016
基于YOLO11n的轻量化猪只目标检测算法
A lightweight algorithm for pig target detection based on YOLO11n
摘要
Abstract
[Objective]To address the low detection accuracy of pig detection algorithms and the limited computational capacity of edge computing devices in large-scale pig farms caused by dense occlusion,illumination variations,and background interference,this study proposes a lightweight pig detection algorithm based on YOLO11n and develops the YOLO11n-MWES model.[Method]Based on YOLO11n,MobileNetV4 was used as a lightweight backbone to reduce model complexity.WTConv was then introduced to improve the original C3K2 feature extraction module by enlarging its receptive field,thereby enhancing the model's ability to extract pig image features in complex scenarios.Additionally,an efficient upsampling module was incorporated into the YOLO11n neck feature fusion network to improve the model's detection accuracy and robustness.Finally,ShapeIoU was adopted as the loss function to accelerate model convergence.[Result]Experimental results showed that YOLO11n-MWES achieved a precision of 98.55%,a recall of 97.57%,and an mAP@0.95 of 80.74%,improving by 0.25,0.87,and 4.74 percentage points,respectively,over YOLO11n.The number of model parameters was reduced by 29.34%,and the FPS was increased by 12.5.Compared with mainstream detection models such as Faster R-CNN,RT-DETR,and YOLOv5,YOLO11n-MWES significantly reduced both false detections and missed detections under occlusion,stacking,background interference,and low-light conditions.[Conclusion]YOLO11n-MWES effectively balances accuracy and lightweight design,making it suitable for edge-based applications such as pig population monitoring and weight estimation in large-scale pig farms.关键词
猪/目标检测/YOLO11n/轻量化Key words
Pig/Object detection/YOLO11n/Lightweight分类
农业科技引用本文复制引用
冯晨,周素茵,徐爱俊,武新梅,杨婷婷,潘科铭,钭一和..基于YOLO11n的轻量化猪只目标检测算法[J].华南农业大学学报,2026,47(4):698-709,12.基金项目
浙江省"三农九方"科技协作计划项目(2025SNJF020) （2025SNJF020）
