@article{ref1,
title="Scene recognition of road traffic accident based on an improved faster R-CNN algorithm",
journal="International journal of crashworthiness",
year="2022",
author="Wang, Fenghui and Qiao, Jie and Li, Lingyi and Liu, Yongtao and Wei, Lang",
volume="27",
number="5",
pages="1428-1432",
abstract="Traffic accident scene recognition is the first premise of accident analysis and scene reconstruction, so the research on traffic accident scene recognition technology is of great significance. In this paper, an improved Region Proposal Network based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed. Because the network adopts four strategies to improve the Regional Proposal Network (RPN), the network structure in this paper is called Multi-strategy Region Proposal Network (MSRPN). The experimental results show that the mAP value of MSRPN algorithm surpasses the other two target recognition algorithms. At the same time, MSRPN only needs to generate 150 region proposals in each image to obtain the above experimental results. In addition, the algorithm has good performance in small target detection. Especially, the target recognition speed is 6 fps, which is faster than other target detection algorithms. In conclusion, the target recognition algorithm based on MSRPN can be effectively applied to target recognition system.<p /> <p>Language: en</p>",
language="en",
issn="1358-8265",
doi="10.1080/13588265.2021.1959156",
url="http://dx.doi.org/10.1080/13588265.2021.1959156"
}