A road abnormity detection model based on window partition and dynamic time regularization

A technology of dynamic time regularization and anomaly detection, which is applied in the direction of roads, roads, road repairs, etc., can solve the problems of different lengths, segmentation, and multi-classification methods that are difficult to apply to binary classification data sets, and achieve good results.

CN109082984AInactive Publication Date: 2018-12-25ZHEJIANG UNIV CITY COLLEGE
4 Cites 10 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2018-12-25
Estimated Expiration
Not applicable · inactive patent

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention relates to a road abnormality detection model based on window partition and dynamic time regularization, comprising the following steps: 1) carrying out threshold detection and sliding window processingon z-axis acceleration data, and screening fragments to be determined; 2) comparing the acceleration data of the segment to be determined with the segments of several known abnormal types and normal road segments through a dynamic time regularization algorithm to obtain a difference degree vector; 3) determining the abnormal type of the segment to be determined. The road abnormality detection model has the beneficial effects that the abnormal section of the road can be intercepted completely, and the experiment result also proves that the abnormal condition of the road can be detected more accurately on different data sets, and the effect of the method is better than that of the existing method in terms of the two-classification or the multi-classification.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention relates to a road detection model, in particular to a road detection model based on window division and dynamic time warping. Background technique

[0002] Highways play an important backbone and pillar role in my country's comprehensive transportation network. By the end of 2016, the total mileage of national highways reached 4.6963 million kilometers. It is estimated that my country's road network will have a scale of about 5.8 million kilometers by 2030. With the rapid development of road construction, the demand for road maintenance and management is also growing rapidly. Governments of various countries will spend huge manpower and material resources on road maintenance. For example, the British government announced that it spent 1.2 billion US dollars on road maintenance in 2017. The City of Toronto spent a total of $6 million on repairing potholes. Road damage can lead to serious traffic accidents, for example, between 2000 and 20...

Examples

Embodiment Construction

[0042] The present invention will be further described below in conjunction with the examples. The description of the following examples is provided only to aid the understanding of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principles of the present invention, some improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0043] The road anomaly detection model mainly performs threshold detection based on the z-axis acceleration generated by the acceleration sensor, and then generates the segment to be determined through the sliding window. Compare the fragment with some standard fragments through the dynamic time warping algorithm to generate a difference degree vector, select the standard fragments corresponding to the k smallest difference degrees in the differen...