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Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey

文献类型：期刊论文

作者Chen, Long1; Lin, Shaobo1; Lu, Xiankai2; Cao, Dongpu3; Wu, Hangbin4; Guo, Chi5; Liu, Chun4; Wang, Fei-Yue6
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2021-06-01
卷号22期号:6页码:3234-3246
ISSN号1524-9050
关键词Deep neural networks autonomous driving vehicle detection pedestrian detection survey
DOI10.1109/TITS.2020.2993926
通讯作者Cao, Dongpu(dongpu@uwaterloo.ca)
英文摘要Vehicle and pedestrian detection is one of the critical tasks in autonomous driving. Since heterogeneous techniques have been proposed, the selection of a detection system with an appropriate balance among detection accuracy, speed and memory consumption for a specific task has become very challenging. To deal with this issue and to provide guidance for model selection, this paper analyzes several mainstream object detection architectures, including Faster R-CNN, R-FCN, and SSD, along with several typical feature extractors, such as ResNet50, ResNet101, MobileNet_V1, MobileNet_V2, Inception_V2 and Inception_ResNet_V2. By conducting extensive experiments using the KITTI benchmark, which is a commonly used street dataset, we demonstrate that Faster R-CNN ResNet50 obtains the best average precision (AP) (58%) for vehicle and pedestrian detection, with a speed of 8.6 FPS. Faster R-CNN Inception_V2 performs best for detecting cars and detecting pedestrians respectively (74.5% and 47.3%). ResNet101 consumes the highest memory (9907 MB) and has the largest number of parameters (64.42 millions), and Inception_ResNet_V2 is the slowest model (3.05 FPS). SSD MobileNet_V2 is the fastest model (70 FPS), and SSD MobileNet_V1 is the lightest model in terms of memory usage (875 MB), both of which are suitable for applications on mobile and embedded devices.
WOS关键词OBJECT DETECTION ; REPRESENTATION ; LOCALIZATION ; RECOGNITION ; REGION ; SCALE
资助项目National Key Research and Development Program of China[2018YFB1305002]
WOS研究方向Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000658360600002
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/45355]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Cao, Dongpu
作者单位1.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China
2.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates
3.Univ Waterloo, Waterloo Cognit Autonomous Driving CogDr Lab, Waterloo, ON N2L 3G1, Canada
4.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China
5.Wuhan Univ, Natl Satellite Positioning Syst Engn Technol Res, Wuhan 430072, Peoples R China
6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Long,Lin, Shaobo,Lu, Xiankai,et al. Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(6):3234-3246.
APA Chen, Long.,Lin, Shaobo.,Lu, Xiankai.,Cao, Dongpu.,Wu, Hangbin.,...&Wang, Fei-Yue.(2021).Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(6),3234-3246.
MLA Chen, Long,et al."Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.6(2021):3234-3246.

入库方式： OAI收割

来源：自动化研究所

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