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Bridgehead bumping rapid detection method based on one-dimensional convolution kernel feature extraction

A bridge-head jumping, feature extraction technology, applied in multi-dimensional acceleration measurement, neural learning method, linear/angular velocity measurement and other directions, can solve the problems of insufficient safety, inconvenient operation, high cost, etc., to save manpower and material resources, rapid detection, The effect of high accuracy

Active Publication Date: 2020-07-03
NINGBO MUNICIPAL ENG CONSTR GROUP +1
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  • Abstract
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AI Technical Summary

Problems solved by technology

The mainstream detection method still stays at manual detection, with low efficiency and insufficient security
In recent years, the measurement methods such as level instrument and turbulence accumulator all have problems such as inconvenient operation and high cost.

Method used

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  • Bridgehead bumping rapid detection method based on one-dimensional convolution kernel feature extraction
  • Bridgehead bumping rapid detection method based on one-dimensional convolution kernel feature extraction
  • Bridgehead bumping rapid detection method based on one-dimensional convolution kernel feature extraction

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Embodiment

[0040] First of all, patrol vehicles need to be equipped with attitude sensors, computer processing terminals and other hardware equipment. When the patrol vehicle passes through several bridgeheads, the attitude sensor transmits the three-dimensional sequence information of the vehicle's speed and acceleration to the computer processing terminal. According to the road industry standard and the time segment of the bridge head jumping phenomenon when passing through several bridge heads, the three-dimensional sequence data is marked in real time, where the mark 0 means that there is no bridge head jumping in this sequence segment, and the mark 1 is the degree of bridge head jumping in this sequence segment Relatively small, mark 2 means that the degree of vehicle jumping at the bridge head of this sequence section is moderate, and mark 3 means that the degree of vehicle jumping at the bridge head of this sequence section is relatively serious. In this embodiment, the patrol veh...

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Abstract

The invention discloses a bridgehead bumping rapid detection method based on one-dimensional convolution kernel feature extraction. The method comprises the following steps: acquiring vehicle motion state data by using a vehicle-mounted attitude sensor, extracting a feature vector from the acquired vehicle motion state data through one-dimensional convolution, and training the feature vector by using a full-connection neural network to obtain a one-dimensional convolutional neural network model; and based on the neural network model, inputting the motion state data of the vehicle passing through the to-be-detected road section into the model, and detecting whether the road section has a bridgehead bump condition and the severity of bridgehead bump.

Description

technical field [0001] The invention relates to the technical field of engineering detection, and more specifically relates to a rapid detection method for bridge head jumping based on one-dimensional convolution kernel feature extraction. Background technique [0002] Vehicle jumping at the bridge head is one of the common road diseases at present. The phenomenon of vehicle jumping at the bridge head refers to the obvious staggered platform at the connection between the bridge head and the road surface structure, which causes the vehicle to jump when passing the bridge head. When the vehicle passes the bridge head jump section, it will produce obvious vibration, which will make the occupants feel obvious discomfort. [0003] Vehicle jumping at the bridge head seriously affects driving safety and comfort. In the section where the vehicle jumps at the bridge head, the driving vehicle has to reduce the speed, thereby reducing the traffic capacity of the highway. Vehicle jum...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G01P15/18G01P3/00
CPCG06N3/08G01P15/18G01P3/00G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 贺斌王战国张丹亚蔡海波朱志强史彦勇舒振宇王钢杨思鹏金海容易顺
Owner NINGBO MUNICIPAL ENG CONSTR GROUP
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