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当前位置: 首页 > 文章 > 基于IFSSD卷积神经网络的柚子采摘目标检测模型 农业机械学报 2020 (5) 28-35+97
Position: Home > Articles > Grapefruit Detection Model Based on IFSSD Convolution Network Transactions of the Chinese Society for Agricultural Machinery 2020 (5) 28-35+97

基于IFSSD卷积神经网络的柚子采摘目标检测模型

作  者：
肖德琴;蔡家豪;林思聪;杨秋妹;谢晓君;郭婉怡
单  位：
关键词：
柚子;采摘;实时检测;深度学习;特征融合;改进的特征融合单镜头检测器
摘  要：
为了解决柚子采摘时传统水果检测模型对于小目标柚子漏检和将叶子误检为膨大期柚子的问题,设计了一种改进的特征融合单镜头检测器(Inception V3-feature fusion single shot-multibox detector,IFSSD)。该检测器以特征融合单发多盒探测器(Feature fusion single shot-multibox detector,FSSD)为基础检测器,以改进的Inception V3网络作为骨干网络代替超深度卷积神经网络(Very deep convolutional networks 16,VGG16),从而提高了计算效率,同时使用Focal Loss损失函数代替Multibox Loss损失函数,进而改善了由于正负样本不平衡导致的检测器误检情况。对测试数据集进行检测,结果表明,该模型的检测准确率为93. 7%(IoU大于0. 5),在单个NVIDIA RTX 2060 GPU上每幅图像检测时间为29 s。本文模型可以实现树上柚子的自动检测。
译  名：
Grapefruit Detection Model Based on IFSSD Convolution Network
作  者：
XIAO Deqin;CAI Jiahao;LIN Sicong;YANG Qiumei;XIE Xiaojun;GUO Wanyi;College of Mathematics and Informatics,South China Agricultural University;School of Medical Information Engineering,Guangzhou University of Chinese Medicine;
关键词：
grapefruit;;picking;;real-time detection;;deep learning;;feature fusion;;IFSSD
摘  要：
The detection,identification and precise positioning of fruit under natural conditions based on machine vision is very important for the development of intelligent picking robots.In order to solve the problem that the traditional fruit detection model for the small target grapefruit missed detection and the leaf was falsely detected as the expansion period grapefruit,an improved feature fusion single multibox detector(InceptionV3-feature fusion single shot-multibox detector,IFSSD) was designed.The feature fusion single multibox detector(feature fusion single shot-multibox detector,FSSD) was used as a base detector and optimized in two ways.On the one hand,the improved InceptionV3 network was used instead of very deep convolutional networks 16(VGG16) as the backbone network to improve computational efficiency.On the other hand,the Focal Loss function was used instead of the Multibox Loss function,which improved the mischeck ingresss of the detector due to the imbalance of positive and negative samples.Finally,the test data set was verified,and the results showed that the model achieved an average accuracy of 93.7%(mAP)(IoU was greater than 0.5).The time of one image was 29s.The model proposed can automatically detect the grapefruit in the grapefruit tree and locate it accurately in real time,which effectively promoted the development of intelligent picking robot.

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