Irregular Target Object Detection Based on Faster R-CNN

Abstract

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.