A Multi-axle and Multi-Type Truck Classification Model for Dynamic Load Recognition
2022-01-0158
03/29/2022
- Event
- Content
- Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions.In recent years, various countries have formulated a series of management methods and governance measures for truck overloading.However, the detection method for overload behavior is not efficient and accurate enough.At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles.Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.Therefore, this paper hopes to design an image processing algorithm to automatically classify different types of trucks, and then calculate the actual load of the trucks in a non-contact way without road modification through the load calculation models of different types of trucks with different axle numbers.For image processing algorithms, machine learning methods are used to classify different trucks, and a matching load calculation model is selected based on the classified categories.For the load calculation model, select and establish different number of axles and different types of load calculation models for dynamic load identification, which are used for load calculation after classification.The method can realize the identification of the load of multi-axle and multi-type trucks, which is used to judge whether it is overloaded, which is of great significance for improving the efficiency and accuracy of overload detection.
- Citation
- Qi, F., "A Multi-axle and Multi-Type Truck Classification Model for Dynamic Load Recognition," SAE Technical Paper 2022-01-0158, 2022, .