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

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, the authors 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 million), 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.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.2993926
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Chen, Lei
  • Lin, Shaobo
  • Lu, Xiankai
  • Cao, Dongpu
  • Wu, Hangbin
  • Guo, Chi
  • Liu, Chun
  • Wang, Fei-Yue
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 3234-3246
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Detection and identification systems; Information processing; Pedestrian safety
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01788729
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 18 2021 12:12PM