IEICE TRANSACTIONS on Fundamentals
Vehicle Key Information Detection Algorithm Based on Improved SSD
Summary :
With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.5 pp.769-779
- Publication Date
- 2020/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAP1135
- Type of Manuscript
- PAPER
- Category
- Intelligent Transport System
Authors
Ende WANG
Shenyang Institute of Automation, Chinese Academy of Sciences
Yong LI
Shenyang Institute of Automation, Chinese Academy of Sciences,Northeastern University
Yuebin WANG
China University of Geosciences
Peng WANG
Northeastern University
Jinlei JIAO
Shenyang Institute of Automation, Chinese Academy of Sciences,Northeastern University
Xiaosheng YU
Northeastern University
Keyword
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Ende WANG, Yong LI, Yuebin WANG, Peng WANG, Jinlei JIAO, Xiaosheng YU, "Vehicle Key Information Detection Algorithm Based on Improved SSD" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 5, pp. 769-779, May 2020, doi: 10.1587/transfun.2019EAP1135.
Abstract: With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAP1135/_p
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@ARTICLE{e103-a_5_769,
author={Ende WANG, Yong LI, Yuebin WANG, Peng WANG, Jinlei JIAO, Xiaosheng YU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Vehicle Key Information Detection Algorithm Based on Improved SSD},
year={2020},
volume={E103-A},
number={5},
pages={769-779},
abstract={With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.},
keywords={},
doi={10.1587/transfun.2019EAP1135},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Vehicle Key Information Detection Algorithm Based on Improved SSD
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 769
EP - 779
AU - Ende WANG
AU - Yong LI
AU - Yuebin WANG
AU - Peng WANG
AU - Jinlei JIAO
AU - Xiaosheng YU
PY - 2020
DO - 10.1587/transfun.2019EAP1135
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2020
AB - With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.
ER -
English
