TY - GEN
T1 - Research on Classifiers Used to Identify Dangerous Goods Transportation Vehicles
AU - Zhang, Haodong
AU - Cheng, Qian
AU - Feng, Kuikui
AU - Jiang, Xiaobei
AU - Wang, Wuhong
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - With the continuous development of the national economy, the domestic demand for dangerous goods has also increased year by year. Once a traffic accident occurs, it will have a huge impact on the natural environment, road safety, and the safety of people’s lives and property. In addition, Advanced Driver Assistance Systems (ADAS) based on sensor technology and advanced control technology provide a good solution for car driving safety. Sensors play a very important role in advanced driver assistance systems. Commonly used sensors mainly include cameras, millimeter wave radars, lidars, etc., which can be used to obtain vehicle internal and external information. This information can help the driver complete the driving task more safely. Therefore, this paper summarizes the current research status of relevant aspects at home and abroad, and compares various vehicle identification and detection algorithms, and uses Haar-features and AdaBoost cascade classifier algorithm to identify dangerous goods transportation vehicles. A total of four classifiers are trained, and the number of positive samples of each classifier is 800, 1200, 1600 and 2000 respectively. Through comparative analysis, it is found that the classifier trained from 1600 positive samples has the best effect.
AB - With the continuous development of the national economy, the domestic demand for dangerous goods has also increased year by year. Once a traffic accident occurs, it will have a huge impact on the natural environment, road safety, and the safety of people’s lives and property. In addition, Advanced Driver Assistance Systems (ADAS) based on sensor technology and advanced control technology provide a good solution for car driving safety. Sensors play a very important role in advanced driver assistance systems. Commonly used sensors mainly include cameras, millimeter wave radars, lidars, etc., which can be used to obtain vehicle internal and external information. This information can help the driver complete the driving task more safely. Therefore, this paper summarizes the current research status of relevant aspects at home and abroad, and compares various vehicle identification and detection algorithms, and uses Haar-features and AdaBoost cascade classifier algorithm to identify dangerous goods transportation vehicles. A total of four classifiers are trained, and the number of positive samples of each classifier is 800, 1200, 1600 and 2000 respectively. Through comparative analysis, it is found that the classifier trained from 1600 positive samples has the best effect.
KW - AdaBoost cascade classifier
KW - Dangerous goods transportation vehicles
KW - Haar-features
UR - http://www.scopus.com/inward/record.url?scp=85121838394&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-5429-9_31
DO - 10.1007/978-981-16-5429-9_31
M3 - Conference contribution
AN - SCOPUS:85121838394
SN - 9789811654282
T3 - Lecture Notes in Electrical Engineering
SP - 411
EP - 422
BT - Green Connected Automated Transportation and Safety - Proceedings of the 11th International Conference on Green Intelligent Transportation Systems and Safety
A2 - Wang, Wuhong
A2 - Chen, Yanyan
A2 - He, Zhengbing
A2 - Jiang, Xiaobei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Green Intelligent Transportation Systems and Safety, 2020
Y2 - 17 October 2020 through 19 October 2020
ER -
