Implementation and acceleration of driving behavior detection model based on Rockchip RV1126 + Yolov5s
Authors: 
Qi Liu, Zhongcheng Wu, Jing Wang, Wentao Peng, Chao LiAuthors Info & Claims
Qi Liu, Zhongcheng Wu, Jing Wang, Wentao Peng, Chao LiAuthors Info & ClaimsPages 902 - 906
Abstract
To prevent tampering with documents before they are deposited into the blockchain, Introducing Sign, a cutting-edge solution designed to address the challenges associated with traditional driving behavior recognition algorithms. Confronting issues such as low recognition accuracy and limited flexibility in complex backgrounds, this paper presents an innovative implementation method leveraging the domestic chip platform Rockchip RV1126.The driving behavior detection model is built upon the YOLOv5s architecture, with crucial modifications to adapt it for deployment on the Rockchip RV1126 chip platform. Specifically, the Focus operator and large-size Maxpool operator in the network are replaced to enhance flexibility and recognition accuracy.
References
[1]
Centerfor Disease Control and Prevention. Distracted Driving [EB/OL]. (2022-01-15) 2023-05-05. https://www.cdc.gov/ transportationsafety/distracted_driving/.
[2]
Parker D, Reason J T, Manstead A S R, Driving errors, driving violations and accident involvement[J]. Ergonomics, 1995, 38(5): 1036-1048.
[3]
Bergasa L M, Nuevo J, Sotelo M A, Real-time system for monitoring driver vigilance[J]. IEEE Transactions on intelligent transportation systems, 2006, 7(1): 63-77.
[4]
Fridman L, Langhans P, Lee J, Driver gaze region estimation without use of eye movement[J]. IEEE Intelligent Systems, 2016, 31(3): 49-56.
[5]
Wang Chang, Guo Yingshi, Fu Rui, Driver's operation behavior when the line of sight leaves the area ahead[J].Journal of Chongqing Jiaotong University(Natural Science Edition),2014,33(03):124-128.)
[6]
ZHENG Zehao, ZHAO Ji,LI Yue. Research on detecting bearing-cover defects based on improved YOLOv3[J]. IEEE Access, 2021,9:10304-10315
[7]
HE Kaiming, ZHANG Xiangyu, REN Shaoqing, Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916
[8]
LI Wenju, CHU Wanghui, CUI Liu, A graph attention feature pyramid network for 3D object detection in point clouds [C]∥2022 7th International Conference on Intelligent Informatics and Biomedical Science (ICIIBMS). Nara: IEEE,2022: 94-98.
[9]
Jia Shina. Research on small target detection algorithm based on improved YOLOv5 [D]. Nanchang: Nanchang University,2023.
[10]
LIU Shu, QI Lu, QIN Haifang, Path aggregation network for instance segmentation[C]∥2018 IEEE/ CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE,2018:8759-8768.
[11]
LI Wenju, CHU Wanghui, CUI Liu, A graph attention feature pyramid network for 3D object detection in point clouds [C]∥2022 7th International Conference on Intelligent Informatics and Biomedical Science (ICIIBMS). Nara: IEEE,2022: 94-98.
[12]
BERGASA L M, NUEVO J, SOTELO M A, Real-time system for monitoring driver vigilance[J]. IEEE Transactions on Intelligent Transportation Systems, 2006,7(1):63-67.
Index Terms
- Implementation and acceleration of driving behavior detection model based on Rockchip RV1126 + Yolov5s
Recommendations
Facial Landmark Detection and Tracking for Facial Behavior Analysis
ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia RetrievalThe face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing ...
Differential Deviation Based Abnormal Driving Behavior Detection
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)According to a report on road safety, more than half of fatal crashes are originated from aggressive drivers. The rear-end collision is the most frequent type of collision in the USA and 87% of them occur due to distracted driving behavior. These ...
Xilinx Adaptive Compute Acceleration Platform: VersalTM Architecture
FPGA '19: Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysIn this paper we describe Xilinx's Versal-Adaptive Compute Acceleration Platform (ACAP). ACAP is a hybrid compute platform that tightly integrates traditional FPGA programmable fabric, software programmable processors and software programmable ...
Comments
Information & Contributors
Information
Published In
January 2024
969 pages
ISBN:9798400716638
DOI:10.1145/3674225
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 31 July 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
PEAI 2024
PEAI 2024: 2024 International Conference on Power Electronics and Artificial Intelligence
January 19 - 21, 2024
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 7Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)3
Reflects downloads up to 24 Dec 2024
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format