基于全卷积神经网络的荔枝表皮缺陷提取
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摘要
【目的】增强荔枝表皮缺陷提取效果,满足其品质检测分级准确性要求。【方法】采用Tensorflow框架构建基于AlexNet的全卷积神经网络AlexNet-FCN,以ReLU为激活函数,Max-pooling为下采样方法,Softmax回归分类器的损失函数作为优化目标,建立荔枝表皮缺陷提取的全卷积神经网络模型,并用批量随机梯度下降法对模型进行优化。【结果】模型收敛后在验证集上裂果交并比(IoUd)为0.83,褐变交并比(IoUb)为0.60,褐变与裂果的总体交并比(IoUa)为0.68;与利用线性SVM、朴素贝叶斯分类器缺陷提取效果相比,该模型的特征提取能力显著提高。【结论】全卷积神经网络在水果表面缺陷提取中具有良好的应用前景。
【Objective】To enhance the effects of litchi fruit pericarp defect extraction and satisfy the accuracy requirements of quality detection and classification.【Method】A fully convolutional neural network was built up based on AlexNet(AlexNet-FCN) using Tensorflow framework, with ReLU as the activation function, Maxpooling as the down-sampling method and loss function of Softmax regression classifier as the optimization target. Mini-batch stochastic gradient descent(Mini-batch SGD) was used to optimize the model.【Result】When the model was converged, the intersection-over-union of dehiscent area(IoUd) of litchi fruit cracking was 0.83 for the validation set, the intersection-over-union of brown area(IoUb) was 0.60, and the intersection-over-union of both dehiscent and brown area(IoUa) was 0.68. Compared with linear-support vector machine(SVM) and Na?ve Bayes classifier, AlexNet-FCN had a stronger defect extraction ability.【Conclusion】Fully convolutional networks(FCN) have a good prospect for application of fruit pericarp defect extraction.
【Objective】To enhance the effects of litchi fruit pericarp defect extraction and satisfy the accuracy requirements of quality detection and classification.【Method】A fully convolutional neural network was built up based on AlexNet(AlexNet-FCN) using Tensorflow framework, with ReLU as the activation function, Maxpooling as the down-sampling method and loss function of Softmax regression classifier as the optimization target. Mini-batch stochastic gradient descent(Mini-batch SGD) was used to optimize the model.【Result】When the model was converged, the intersection-over-union of dehiscent area(IoUd) of litchi fruit cracking was 0.83 for the validation set, the intersection-over-union of brown area(IoUb) was 0.60, and the intersection-over-union of both dehiscent and brown area(IoUa) was 0.68. Compared with linear-support vector machine(SVM) and Na?ve Bayes classifier, AlexNet-FCN had a stronger defect extraction ability.【Conclusion】Fully convolutional networks(FCN) have a good prospect for application of fruit pericarp defect extraction.
引文
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[2]赵茂程,侯文军.我国基于机器视觉的水果自动分级技术及研究进展[J].包装与食品机械,2007(5):5-8.
[3]饶秀勤.基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[D].杭州:浙江大学,2007.
[4]CEN H Y,LU R F,ZHU Q B,et al.Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification[J].Postharvest Biol Tec,2016,111:352-361.
[5]熊俊涛,邹湘军,刘念,等.基于机器视觉的荔枝果实采摘时品质检测技术[J].农业机械学报,2014,45(7):54-60.
[6]杜永忠,平雪良,何佳唯.圣女果表面缺陷检测与分级系统研究[J].农业机械学报,2013,44(S1):194-199.
[7]赵娟,彭彦昆,SAGAR D,等.基于机器视觉的苹果外观缺陷在线检测[J].农业机械学报,2013,44(S1):260-263.
[8]李江波,饶秀勤,应义斌.基于照度-反射模型的脐橙表面缺陷检测[J].农业工程学报,2011,27(7):338-342.
[9]MILLER B.Peach defect detection with machinevision[J].Trans Asae,1991,34(6):2588-2597.
[10]田杰,韩冬,胡秋霞,等.基于PCA和高斯混合模型的小麦病害彩色图像分割[J].农业机械学报,2014,45(7):267-271.
[11]LEEMANS V,MAGEIN H,DESTAIN M F.Defect segmentation on‘Jonagold’apples using colour vision and a Bayesian classification method[J].Comput Electron Agr,1999,23(1):43-53.
[12]MIZUSHIMA A,LU R.An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method[J].Comput Electron Agr,2013,94:29-37.
[13]YANG W,JIN L,TAO D,et al.Dropsample:A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition[J].Pattern Recogn,2016,58(4):190-203.
[14]ALBU R D.Human face recognition using convolutional neural networks[J].JEEE,2009(2):110-113.
[15]RAMAIAH N P,IJJINA E P,MOHAN C K.Illumination invariant face recognition using convolutional neural networks[C]//IEEE International Conference on Signal Processing,US:IEEE Xplore,2015:1-4.
[16]SINGH R,OM H.Newborn face recognition using deep convolutional neural network[J].Multimed Tools Appl,2017,76(18):1-11.
[17]高震宇,王安,刘勇,等.基于卷积神经网络的鲜茶叶智能分选系统研究[J].农业机械学报,2017,48(7):53-58.
[18]马晓丹,关海鸥,祁广云,等.基于改进级联神经网络的大豆叶部病害诊断模型[J].农业机械学报,2017,48(1):163-168.
[19]谭文学,赵春江,吴华瑞,等.基于弹性动量深度学习神经网络的果体病理图像识别[J].农业机械学报,2015,46(1):20-25.
[20]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].TPAMI,2014,39(4):640-651.
[21]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 15th International Conference on Neural Information Processing Systems.Cambridge:Mit Press,2012:1097-1105.
[22]陈石榕.国际鲜荔枝标准[J].标准化信息,1997(4):11-12.
[23]周先治,陈阳,唐建阳,等.荔枝褐变损失估计研究[J].热带作物学报,2012,33(8):1403-1408.
[24]ZEILER M D,KRISHNAN D,TAYLOR G W,et al.Deconvolutional networks[C]//Computer Vision and Pattern Recognition.US:IEEE Computer Science,2010:2528-2535.