Research Article
A Target Detection Algorithm Based on Faster R-CNN
@INPROCEEDINGS{10.1007/978-3-030-69066-3_44, author={XinQing Yan and YuHan Yang and GuiMing Lu}, title={A Target Detection Algorithm Based on Faster R-CNN}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Target detection ResNet-101 Faster R-CNN}, doi={10.1007/978-3-030-69066-3_44} }- XinQing Yan
YuHan Yang
GuiMing Lu
Year: 2021
A Target Detection Algorithm Based on Faster R-CNN
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_44
Abstract
Target detection is one of the hotspots of image processing research. In the image, due to factors such as distance or light, it will affect the target detection result and increase the error detection rate. Moreover, the existing network training time is too long to meet the actual needs. In order to reduce the lack of light or shadow interference and other factors, based on the Faster R-CNN framework, this paper innovatively proposes a method to improve its feature network ResNet-101 to extract deep features of images.In order to shorten the running time, this paper introduces the region number adjustment layer to adaptively adjust the number of candidate regions selected by RPN during the training process. This paper conducts experiments on the PASCAL VOC data set. The experimental results show that the improved feature network model proposed has an accuracy improvement of 2% compared with the original feature network model. The results show that the target detection algorithm proposed in this paper has higher recognition accuracy than the original algorithm.