文献类型：期刊文献

英文题名：DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

作者：Liang, Tianjiao[1,2];Bao, Hong[1,2];Pan, Weiguo[1,2];Fan, Xinyue[1,2];Li, Han[1,2]

第一作者：Liang, Tianjiao

通讯作者：Pan, WG[1];Pan, WG[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, 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

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, 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：2022

卷号：22

期号：13

外文期刊名：SENSORS

收录：;EI(收录号：20222612286037);Scopus(收录号：2-s2.0-85132843302);WOS:【SCI-EXPANDED(收录号:WOS:000823652200001)】；

基金：This research was funded by the National Natural Science Foundation of China (Nos. 61802019, 61932012, 61871039, 61906017 and 62006020) and the Beijing Municipal Education Commission Science and Technology Program(Nos. KM201911417003, KM201911417009 and KM201911417001). Beijing Union University Research and Innovation Projects for Postgraduates (No.YZ2020K001). By the Premium Funding Project for Academic Human Resources Development in Beijing Union University under Grant BPHR2020DZ02.

语种：英文

外文关键词：autonomous driving; deep learning; object detection; transformer

摘要：Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively.