文献类型：期刊文献

英文题名：A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

作者：Liu, Teng[1];Xu, Cheng[1];Liu, Hongzhe[1];Li, Xuewei[1];Wang, Pengfei[2]

通讯作者：Liu, HZ[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Minist Emergency Management Peoples Republ China, Commun & Informat Ctr, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2021

卷号：2021

外文期刊名：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录：;EI(收录号：20215011311413);Scopus(收录号：2-s2.0-85120833532);WOS:【SCI-EXPANDED(收录号:WOS:000770605600001)】；

基金：This work was supported by the Beijing Municipal Commission of Education Project (Nos. KM202111417001 and KM201911417001), the National Natural Science Foundation of China (Grant Nos. 61871039, 62102033, 62171042, 62006020, and 61906017), the Collaborative Innovation Center for Visual Intelligence (Grant No. CYXC2011), and the Academic Research Projects of Beijing Union University (Nos. BPHR2020DZ02, ZB10202003, ZK40202101, and ZK120202104).

语种：英文

外文关键词：Deep learning - Extraction - Feature extraction - Smart city - Vehicle to Everything - Vehicles

摘要：Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.