改进YOLOv8n的群养生猪目标检测算法研究
Research on group pig target detection with improve YOLOv8n algorithm
钟长华 1宋弘 2吴浩 1江俊卓1
作者信息
- 1. 四川轻化工大学自动化与信息工程学院,四川 宜宾 644000;人工智能四川省重点实验室,四川 宜宾 644000
- 2. 阿坝师范学院,四川 阿坝 623002
- 折叠
摘要
为满足嵌入式设备对模型轻量化和高精度检测的需求,本研究提出了一种基于YOLOv8n模型改进的群养生猪目标检测算法.首先在主干网络中引入C2fFB结构,减少对内存的访问量与冗余计算;然后以BiFPN结构构成新的Neck网络并引入C2fSC模块,进一步实现更深层次的特征融合,减少融合的空间冗余和通道冗余;最后采用SIoU替换原来的CIoU,提高了模型的精度.实验结果表明,本算法的F1 分数、查准率、查全率、平均精确度相比改进之前分别提升了 3%、1.8%、3.5%、1.5%,参数量、计算量、模型大小相比改进之前分别下降了46.84%、27.16%、44.71%.因此,本算法模型为群养生猪的智慧养殖提供了1种高效的目标检测解决方案.
Abstract
In order to meet the needs of for model lightweight and high-precision detection for embedded devices,this paper proposed a group-raising pig target detection algorithm based on the improved YOLOv8n model.First,the C2fFB structure was introduced in the backbone network to reduce the amount of memory access and redundant calculations.Then the new Neck network was constructed with the BiFPN structure and the C2fSC module was introduced to further achieve deeper feature fusion and reduce of the spatial redundancy and channel redundancy of the fusion.Finally,SIoU was used to replace the original CIoU to improve the accuracy of the model.The experimental results showed that the F1 score,precision,recall rate,and average precision of the proposed algorithm were improved compared with the original algorithm by 3%,1.8%,3.5%,and 1.5%,respectively.And the number of parameters,calculation amount,and model size were reduced by 46.84%,27.16%,and 44.71%respectively.Therefore,the algorithm model in this paper provides an efficient target detection solution for the intelligent breeding of group-raising pigs.
关键词
目标检测/YOLOv8n/群养生猪/BiFPN/交并比Key words
object detection/YOLOv8n/group pigs/BiFPN/intersection ratio引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据