摘要
以粉果番茄为试验材料,基于深度学习方法开展了番茄果实成熟度和外观品质的检测研究。试验中共采集番茄图片数据2036张,通过处理扩增至5316张,然后将数据进行标注和文件转换,构建了试验用数据集;通过在YOLOv5s模型中加入CA注意力机制、替换Stem block结构、结合识别需求优化检测层尺度、替换K-means++聚类算法来实现SC-YOLOv5s识别精度提升,提高模型的特征表达能力;通过在SC-YOLOv5s模型中加入Fire module结构进行轻量化卷积、降低Bottleneck模块的参数量来实现SC-YOLOv5s-lite轻量化设计,提升模型在硬件上的检测速度;将SC-YOLOv5s-lite模型在训练集上进行训练优化、消融试验和性能对比,结果表明,SC-YOLOv5s-lite模型内存大小为7.73 M,其准确率为89.04%,召回率83.35%,平均精度91.34%,检测时间为143 ms,相比于YOLOv5s,模型参数量降低了45.57%,模型大小压缩了44.86%,平均精度提升3.98%,检测时间减少20.99%,优势明显,更适合于硬件上部署。
The study was conducted on the detection of tomato fruit maturity and appearance quality based on deep learning methods using pink tomato as the experimental material.Two thousand and thirty-six tomato image data were collected and amplified to 5316 through preprocessing.Then,the data was annotated and converted into files to construct an experimental dataset.The experiment improves the accuracy of SC-YOLOv5s by adding CA attention mechanism,replacing the Stem block structure,optimizing the detection layer scale based on recognition requirements,and replacing the K-means++clustering algorithm to improve the model’s feature expression ability.By adding a fire module structure to SC-YOLOv5s for lightweight convolution and reducing the parameter count of the Bottleneck module,the SC-YOLOv5s-lite lightweight design is achieved,improving the detection speed of the model on hardware;Train and optimize the SC-YOLOv5s-lite model on the training set.The results showed that the memory usage of the SC-YOLOv5s-lite model was 7.73 M,with an accuracy rate of 89.04%,a recall rate of 83.35%,an average accuracy of 91.34%,and a detection time of 143 ms.Compared to YOLOv5s,the model parameter quantity is reduced by 54.57%,model size is compressed by 44.86%,with an average accuracy improvement of 3.98%,and the detection time is reduced by 20.99%.It has obvious advantages and is more suitable for hardware deployment.
作者
孙宇朝
李守豪
夏秀波
杨玮
李民赞
张焕春
SUN Yuchao;LI Shouhao;XIA Xiubo;YANG Wei;LI Minzan;ZHANG Huanchun(Yantai Academy of Agricultural Sciences in Shandong Province,Yantai,Shandong 265500,China;Key Laboratory of Smart Agriculture Systems,Ministry of Education,China Agricultural University,Beijing 100083,China;Yantai Smart Agriculture Research Center,Yantai,Shandong 265500,China)
出处
《园艺学报》
CAS
CSCD
北大核心
2024年第2期396-410,共15页
Acta Horticulturae Sinica
基金
山东省现代优势产业集群+人工智能试点示范单位项目(鲁工信工联〔2020〕89号)
烟台市设施番茄育种攻关团队项目(烟农[2023]174号)。
关键词
番茄
成熟度
外观品质
检测
深度学习
计算机视觉
tomato
maturity
appearance quality
detection
deep learning
computer vision
作者简介
通信作者:夏秀波,E-mail:xiuboxia@163.com;通信作者:杨玮,E-mail:cauyw@cau.edu.cn。
