This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Robust Sensor Fused Object Detection Using Convolutional Neural Networks for Autonomous Vehicles
Technical Paper
2020-01-0100
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module. The proposed MSFD system employs the principles of artificial intelligence beyond simple comparison of data variance of the sensors. And then, its performance is further improved by using the temporal correlation information with an adaptive threshold scheme. The proposed system is evaluated with the collected video data (6,854 image frames with 18,918 labeled objects). Based on the laboratory testing and in-vehicle validation, the proposed system demonstrates its high accuracy for detecting any size of objects in real-world data.
Recommended Content
Authors
Topic
Citation
Park, J., Jayachandran Raguraman, S., Aslam, A., and Gotadki, S., "Robust Sensor Fused Object Detection Using Convolutional Neural Networks for Autonomous Vehicles," SAE Technical Paper 2020-01-0100, 2020, https://doi.org/10.4271/2020-01-0100.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Table 3 |
Also In
References
- Bi , X. , Tan , B. , Xu , Z. , and Huang , L. A New Method of Target Detection Based on Autonomous Radar and Camera Data Fusion SAE Technical Paper 2017-01-1977 2017 https://doi.org/10.4271/2017-01-1977
- Wang , T. , Zheng , N. , Xin , J. , and Ma , Z. Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications Sensors 2011
- Kim , D.Y. and Jeon , M. Data Fusion of Radar and Image Measurements for Multi-Object Tracking via Kalman Filtering Information Sciences 278 641 652 10.1016/j.ins.2014.03.080
- Redmon , J. , Divvala , S. , Girshick , R. , and Farhadi , A. You Only Look Once:Unified, Real-Time Object Detection IEEE Conference on CVPR 2016 779 788
- Ren , S. , He , K. , Girshick , R. , and Sun , J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks IEEE Transactions on Pattern Analysis and Machine Intelligence 39 6 1137 1149 1 June 2017 10.1109/TPAMI.2016.2577031
- Liu , W. , Anguelov , D. , Erhan , D. , Szegedy , C. et al. 2016 arXiv:1512.02325
- Szegedy , C. , Liu , W. , Jia , Y. , Sermanet , P. et al. Going Deeper With Convolutions The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1 9 2015
- Autonomou Stuff https://www.autonomoustuff.com/wp-content/uploads/2016/08/delphi_esr_data_sheet.pdf
- Howard , A. , Zhu , M. , Chen , B. , Kalenichenko , D. et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 2017
- https://github.com/chuanqi305/MobileNet-SSD