期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：4
页码：1-6
DOI：10.1088/1755-1315/252/4/042111
出版社：IOP Publishing
摘要：Forthe shortcomings of traditional target detection algorithms can only extract specific target features for detection, propose the Faster R-CNN target detecti-on model of deep learning, combined with VGG16 and ResNet101 convolutional neur-al network methods, to detection of irregular target objects. Experiments established two types of irregular target data sets, walnut and jujube, use the network training and testing, verified the feasibility of deep learning network for detecting irregular target objects. The experimental results show that the Faster R-CNN target detection networ-kof training on the self-built data set, the final detection result reaches 95%, which proves the effectiveness of the network for detecting irregular target objects.
