基于改进EfficientDet的油橄榄果实成熟度检测  

Detection of the olive fruit maturity based on improved EfficientDet

作　　者：陈锋军[1,2,3] 张新伟 朱学岩 李志强 林剑辉 Chen Fengjun;Zhang Xinwei;Zhu Xueyan;Li Zhiqiang;Lin Jianhui(School of Technology,Beijing Forestry University,Beijing 100083,China;Beijing Laboratory of Urban and Rural Ecological Environment,Beijing 100083,China;Key Laboratory of State Forestry Administration for Forestry Equipment and Automation,Beijing 100083,China;Research Center for Intelligent Forestry,Beijing 100083,China)

机构地区：[1]北京林业大学工学院,北京100083 [2]城乡生态环境北京实验室,北京100083 [3]国家林业局林业装备与自动化国家重点实验室,北京100083 [4]智慧林业研究中心,北京100083

出　　处：《农业工程学报》2022年第13期158-166,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划(2019YFD1002401);中央高校基本科研业务费专项资金资助(2021ZY74);北京市共建项目。

摘　　要：自然环境下自动准确地检测油橄榄果实的成熟度是实现油橄榄果实自动化采摘的基础。该研究根据成熟期油橄榄果实表型特征的变化以及参考国际油橄榄理事会和中国林业行业标准的建议制定了油橄榄果实成熟度标准,并针对油橄榄果实相邻成熟度特征差异不明显以及果实之间相互遮挡问题,提出一种改进EfficientDet的油橄榄果实成熟度检测方法。首先改进特征提取网络,在特征提取网络中引入卷积注意力模块(Convolution Block Attention Module,CBAM)细化不同成熟度之间的特征映射;其次改进特征融合网络,在加权双向特征金字塔网络(BidirectionalFeaturePyramid Network,Bi-FPN)中增加跨级的数据流加强果实的相对位置信息,最后通过623幅油橄榄测试图像对改进的EfficientDet模型进行测试。改进EfficientDet模型在测试集下的精确率P、召回率R和平均精度均值m AP分别为92.89%、93.59%和94.60%,平均检测时间为0.337s,模型大小为32.4M。对比SSD、EfficientDet、YOLOv3、YOLOv5s和FasterR-CNN模型,平均精度均值m AP分别提升7.85、4.77、3.73、1.15和1.04个百分点。改进EfficientDet模型能够为油橄榄果实的自动化采摘提供有效探索。The picking maturity of olive fruit varies greatly in different uses. However, the commonly-used manual picking cannot fully meet the picked fruit in the best picking period, due to the low picking efficiency and high cost during olive fruit picking. The purpose of this study was to determine the distribution areas of fruits in the different maturity under the natural environment, in order to realize the automatic picking of olive fruits. Firstly, the phenotypic characteristics of olive fruit at maturity were determined to develop the new standard of ripeness, according to the recommendations of the International Olive Council and China’s forestry industry standards. Secondly, mobile phones were utilized to capture the olive images at the different maturity periods under natural conditions. An olive image dataset was then constructed. A series of operations were implemented to adjust the contrast, the image size, salt-pepper noise, and artificial occlusion, in order to expand the olive images to four times the original ones. The training and test set was built at the ratio of 9:1. Thirdly, an improved EfficientDet was proposed to detect the ripeness of olive fruits, the inconspicuous changes of the adjacent ripeness features, and the mutual occlusion between the fruits. The main advantages of this model were as follows: 1) A module of convolution block attention was introduced into the feature extraction network of the EfficientDet model. The weight distribution of features was then calculated in the channel and spatial domain. The improved model was then used to accurately locate the target area, thereby eliminating the interference of background information. The phenotypic characteristics of olive fruit were fully extracted for the key features to distinguish maturity categories. 2) A cross-level data flow was added to fuse the features of the bottom nodes into the high-level nodes for common learning. As such, the weighted bidirectional feature pyramid network of the EfficientDet model fully utilized the sem

关 键 词：油橄榄果实 成熟度检测 EfficientDet 注意力机制 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]