MCBPnet:一种高效的轻量级青杏识别模型  

MCBPnet as an efficient and lightweight recognition model for green apricot fruits

作　　者：师翊 王应宽 王菲 卿顺浩 赵龙[4] 宇文星璨 SHI Yi;WANG Yingkuan;WANG Fei;QING Shunhao;ZHAO Long;YUWEN Xingcan(Academy of Agricultural Planning and Engineering,Ministry of Agriculture and Rural Affairs,Beijing 100125,China;College of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471003,China;Chinese Society of Agricultural Engieering,Beijing 100125,China;College of Horticulture and Plant Protection,Henan University of Science and Technology,Luoyang 471003,China)

机构地区：[1]农业农村部规划设计研究院,北京100125 [2]河南科技大学农业装备工程学院,洛阳471003 [3]中国农业工程学会,北京100125 [4]河南科技大学园艺与植物保护学院,洛阳471003

出　　处：《农业工程学报》2025年第5期156-164,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：农业农村部农业农村信息化宣传推广项目(A150101);国家自然科学基金项目(52309050);中国科技期刊卓越行动计划二期-《农业工程学报》中文领军期刊项目(卓越二期-B1-036);河南科技大学青年骨干教师项目(4004-13450010);河南省重点研发与推广专项(科技攻关)项目(232102110264,222102110452);中国科学技术协会农村专业技术服务中心资助类项目(20230508ZZ06210034);河南省高等学校重点科研项目(24B416001)。

摘　　要：为解决青杏识别易受田间复杂环境、设备计算资源等限制的问题,该研究提出一种MCBPnet轻量化青杏识别模型。该研究使用MobileNetV3轻量化网络结构代替YOLOv8的主干特征提取网络,降低了模型的复杂程度;在MobileNetV3网络中融入CBAM(convolutional block attention module),在颈部网络引入了BiFPN(bi-directional feature pyramid network)结构,提高模型对青杏图像的特征提取和融合的能力;检测头部分采用了PConv(partial convolution)结构,以提高模型的鲁棒性和检测精度。将MCBPnet模型应用于青杏检测试验,结果表明,MCBPnet模型的检测速度为109.890帧/s,与YOLOv8n模型相比提高了70.33%,模型运算量为6.1 G,为YOLOv8n模型的75.31%,并且精确度(precision,P)和平均精度值(mean average precision,mAP50)达到了0.988和0.994,模型具有较高的检测精度,同时实现了模型的轻量化。MCBPnet模型实现了对青杏果实的高效、精确的实时检测,为青杏的自动化识别和采摘提供了技术支持。Intelligent and automation technologies have been widely applied to agricultural production in recent years.A green apricot is one of the most significant economic fruits in Asian areas.However,traditional identification cannot fully meet largescale production under complex environmental conditions in precision management.Fruit recognition and detection technologies can be required to enhance the accuracy and efficiency of large-scale production at present.In this study,an efficient and lightweight target recognition model(MCBPnet)was developed to automatically detect the greet apricot fruits using advanced deep learning.The CBAM(convolutional block attention module)was first integrated into the MobileNetV3 architecture.The precision of the model was significantly enhanced to identify the apricot fruits with the most rich image areas.The regions of an image were then prioritized with the most relevant to fruit detection.More computational resources were effectively allocated to the features closely associated with the task.As a result,the model structure,IRCBAM(inverted residual convolutional block attention module)was then developed to incorporate as the backbone network of MCBPnet.BiFPN(Bi-directional feature pyramid network)was also introduced into the neck network,in order to further enhance the performance of the model.A feature pyramid was constructed to capture the multi-scale features from images using the BiFPN model.The fruits with varying sizes were then detected within a single frame.A high detection rate was also maintained in a wide range of fruit sizes and orientations.Moreover,some occlusions were also removed during detection,where the fruits were partially obscured by leaves or other fruits.The PConv(partial convolution)structure was utilized in the detection head.The high accuracy of detection was achieved,even when only a portion of a fruit was visible.As such,reliable recognition was obtained to increase its accuracy and robustness under conditions of partial visibility.The results demonstrat

关 键 词：识别 目标检测 计算机视觉 轻量化模型 青杏 自动化农业 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] S24[自动化与计算机技术—计算机科学与技术]