A pavement type estimation method based on depth convolution neural network without loss function

Abstract

The invention discloses a method for estimating pavement type based on depth convolution neural network without loss function, which comprises the following steps: step 1, collecting pavement workingcondition image, calibrating pavement type and establishing pavement working condition database; The depth convolutional neural network based on loss-free function is trained to obtain image features,and then binary hash coding and histogram processing are performed to obtain image feature output vectors. According to the feature output vector of the image and the corresponding road type, the support vector machine is trained and the parameters are selected, and the road type discrimination function is determined. 2, collect that working condition image of the pavement to be tested, obtainingthe characteristic output vector of the pavement to be tested according to the step 1, and determine the type of the pavement to be tested by using the trained support vector machine. It simplifies the feature extraction of convolution neural network depth learning model, and uses support vector machine to classify images, which greatly reduces the difficulty of convolution training and improvesthe efficiency of classification.

Description

technical field [0001] The present invention relates to the field of automobile powertrain control, and more specifically, the present invention relates to a road surface type estimation method based on a deep convolutional neural network without loss function. Background technique [0002] The road surface adhesion coefficient is an important parameter of the vehicle's active safety control strategy. Assuming that the parameter value can be estimated in real time, the control system can adjust the control strategy in real time according to the current road surface conditions and vehicle driving status, so as to avoid traffic accidents caused by poor road adhesion conditions. Accidents, improve the safety, handling, economy and comfort of the car. [0003] At present, most scholars at home and abroad use commonly used on-board sensors to measure the motion response of the car body or wheels when driving on different roads to estimate the road adhesion coefficient, but this e...

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Problems solved by technology

[0003] At present, most scholars at home and abroad use commonly used on-board sensors to measure the motion response of the car body or wheels when driving on different roads to estimate the road adhesion coefficient, but this estimation method

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