Liu Gaochao, Kang Lijun, Dai Yongqiang
College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.
Sensors (Basel). 2025 Jul 25;25(15):4610. doi: 10.3390/s25154610.
Sheep face recognition technology is critical in key areas such as individual sheep identification and behavior monitoring. Existing sheep face recognition models typically require high computational resources. When these models are deployed on mobile or embedded devices, problems such as reduced model recognition accuracy and increased recognition time arise. To address these problems, an improved Parameter Fusion Lightweight You Only Look Once (PFL-YOLO) sheep face recognition model based on YOLOv8n is proposed. In this study, the Efficient Hybrid Conv (EHConv) module is first integrated to enhance the extraction capability of the model for sheep face features. At the same time, the Residual C2f (RC2f) module is introduced to facilitate the effective fusion of multi-scale feature information and improve the information processing capability of the model; furthermore, the Efficient Spatial Pyramid Pooling Fast (ESPPF) module was used to fuse features of different scales. Finally, parameter fusion optimization work was carried out for the detection head, and the construction of the Parameter Fusion Detection (PFDetect) module was achieved, which significantly reduced the number of model parameters and computational complexity. The experimental results show that the PFL-YOLO model exhibits an excellent performance-efficiency balance in sheep face recognition tasks: mAP@50 and mAP@50:95 reach 99.5% and 87.4%, respectively, and the accuracy is close to or equal to the mainstream benchmark model. At the same time, the number of parameters is only 1.01 M, which is reduced by 45.1%, 83.7%, 66.6%, 71.4%, and 61.2% compared to YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv9-t, and YOLO11n, respectively. The size of the model was compressed to 2.1 MB, which was reduced by 44.7%, 82.5%, 65%, 72%, and 59.6%, respectively, compared to similar lightweight models. The experimental results confirm that the PFL-YOLO model maintains high accuracy recognition performance while being lightweight and can provide a new solution for sheep face recognition models on resource-constrained devices.
绵羊面部识别技术在个体绵羊识别和行为监测等关键领域至关重要。现有的绵羊面部识别模型通常需要高计算资源。当这些模型部署在移动或嵌入式设备上时，会出现模型识别准确率降低和识别时间增加等问题。为了解决这些问题，提出了一种基于YOLOv8n的改进型参数融合轻量级单阶段检测器（PFL-YOLO）绵羊面部识别模型。在本研究中，首先集成了高效混合卷积（EHConv）模块，以增强模型对绵羊面部特征的提取能力。同时，引入了残差C2f（RC2f）模块，以促进多尺度特征信息的有效融合，提高模型的信息处理能力；此外，使用高效空间金字塔池化快速（ESPPF）模块融合不同尺度的特征。最后，对检测头进行了参数融合优化工作，实现了参数融合检测（PFDetect）模块的构建，显著减少了模型参数数量和计算复杂度。实验结果表明，PFL-YOLO模型在绵羊面部识别任务中表现出优异的性能-效率平衡：mAP@50和mAP@50:95分别达到99.5%和87.4%，准确率接近或等于主流基准模型。同时，参数数量仅为1.01M，与YOLOv5n、YOLOv7-tiny、YOLOv8n、YOLOv9-t和YOLO11n相比，分别减少了45.1%、83.7%、66.6%、71.4%和61.2%。模型大小压缩至2.1MB，与类似轻量级模型相比，分别减少了44.7%、82.5%、65%、72%和59.6%。实验结果证实，PFL-YOLO模型在保持高精度识别性能的同时具有轻量级特点，可为资源受限设备上的绵羊面部识别模型提供新的解决方案。
