摘要
针对复杂背景下茶叶嫩芽识别率低,鲁棒性差等问题,提出一种改进YOLOv8的茶叶嫩芽检测算法。通过引入Swin Transformer自注意力机制构建CTS特征提取模块,以提升模型全局特征提取能力;借鉴多尺度融合思想构建ExFModule模块,在丰富语义特征信息的同时,使网络能够自适应选择有用特征而抑制无用特征;在特征融合方面,提出一种BFPAN特征图拼接方法,让模型能够更加关注小目标特征,提升模型的特征融合能力。实验结果表明,改进后的YOLOv8算法,在茶叶嫩芽数据集上的平均精度达到93.4%,相比改进前提升了4.4%,且检测速度基本保持不变,能够实现快速准确的茶叶嫩芽识别检测,可为茶叶嫩芽的智能化采摘提供技术支持。
Aiming at the problems of low recognition rate and poor robustness of tea buds in complex backgrounds,an improved YOLOv8 tea bud detection algorithm is proposed.By introducing the Swin Transformer self attention mechanism,a CTS feature extraction module is constructed to enhance the global feature extraction capability of the model;Drawing on the idea of multi-scale fusion,constructing the ExFModule module enriches semantic feature information while enabling the network to adaptively select useful features and suppress useless ones;In terms of feature fusion,a BFPAN feature map stitching method is proposed to enable the model to pay more attention to small target features and improve its feature fusion ability.The experimental results show that the improved YOLOv8 algorithm achieves an average accuracy of 93.4%on the tea tender bud dataset,an increase of 4.4%compared to before improvement,and the detection speed remains basically unchanged.It can achieve fast and accurate tea tender bud recognition and detection,and provide technical support for the intelligent picking of tea tender buds.
作者
潘海鸿
陈希良
钱广坤
申毅莉
陈琳
PAN Haihong;CHEN Xiliang;QIAN Guangkun;SHEN Yili;CHEN Lin(School of Mechanical Engineering,Guangxi University,Nanning 530004,China;School of Mechanical and Resource Engineering,Wuzhou University,Wuzhou Guangxi 543002,China)
出处
《激光杂志》
CAS
北大核心
2024年第11期65-70,共6页
Laser Journal
基金
广西创新驱动发展专项(No.桂科AA18118002)
梧州市科学研究与技术开发计划项目(No.202202064、No.202202039)。
