Irregular Target Object Detection Based on Faster R-CNN

[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.

[发布机构] Zhengzhou University, Zhengzhou; 450006, China^1

[效力级别] 材料科学

[关键词] Data set;Learning network;Network methods;Network training;Target data set;Target detection algorithm;Target feature;Target object