基于卷积神经网络的偏色光下植物图像分割方法  

Plant image segmentation method under bias light based on convolutional neural network

作　　者：张文彬[1] 朱敏 张宁[1] 董乐[1] ZHANG Wenbin;ZHU Min;ZHANG Ning;DONG Le(School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu Sichuan 611731,China;Zhuhai(Hengqin)Food Safety Research Institute,Zhuhai Guangdong 519000,China)

机构地区：[1]电子科技大学计算机科学与工程学院,成都611731 [2]珠海(横琴)食品安全研究院,广东珠海519000

出　　处：《计算机应用》2019年第12期3665-3672,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61772114);广东省引进领军人才项目(2016LJ06S419)~~

摘　　要：为了解决传统图像分割算法在植物工厂中偏色光植物图像上分割精确度不高、泛化性能差的问题,提出了一种基于卷积神经网络,并结合深度学习技术,对人工偏色光下植物图像进行精确分割的方法。采用该方法,最终在偏色光植物图像原始测试集上达到了91.89%的分割精确度,远超全卷积网络、聚类、阈值、区域生长等分割算法。此外,在不同色光之下的植物图片上进行测试,该方法也较上述其他分割算法有着更好的分割效果和泛化性能。实验结果表明,所提方法能够显著提高偏色光下植物图像分割的精确度,可以应用于实际的植物工厂工程项目当中。To solve the problems of low precision and poor generalization performance of traditional image segmentation algorithms on the plant images under bias light in plant factory, a method based on neural network and deep learning for accurately segmenting the plant images under artificial bias light in plant factory was proposed. By using this method, the segmentation accuracy on the original test set of bias light plant images is 91.89% and is far superior to that by other segmentation algorithms such as Fully Convolutional Network(FCN), clustering, threshold and region growth. In addition, this method has better segmentation effect and generalization performance than the above methods on plant images under different color lights. The experimental results show that the proposed method can significantly improve the accuracy of plant image segmentation under bias light, and can be applied to practical plant factory projects.

关 键 词：植物工厂 深度学习 卷积神经网络 偏色光植物图像 图像分割 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]