Road snow and rain state automatic identification method based on feature information classification

Abstract

A road snow and rain state automatic identification method based on feature information classification comprises a step A of extracting a sample describing feature and constructing a Bayes classifier, and a step B of detecting the road state. The step A comprises a small step A 1 of acquiring a sample image, a small step A 2 of preprocessing the image, a small step A 3 of extracting a sampling image texture attribute value and an average gray value of a road effective coverage sample, a small step A 4 of calculating a probability density function of the road sample, and a small step A 5 of determining an operational rule of a type conditional probability density function to construct the Bayes classifier. The step B comprises a small step B 1 of acquiring a road detecting image, a small step B 2 of preprocessing the image, a small step B 3 of extracting a texture attribute value and an average gray value of a road detecting standard image frame, and a small step B 4 of judging the road surface state of the road. The road snow and rain state automatic identification method based on the feature information classification can adapt to various weather variations and complexity and changes of traffic road states, detection efficiency and accuracy are high, and cost is low, so that the road snow and rain state automatic identification method based on the feature information classification provides reference for traffic safety guarantee and traffic management.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and relates to image / video information processing and pattern recognition technology, in particular to a method for automatic recognition of road surface rain and snow states. Background technique [0002] The frequency of traffic accidents under severe weather conditions is high, especially in rainy and snowy weather. Especially for expressways, rain and snowfall have become a major factor leading to traffic accidents, so it is necessary to detect road (expressway ) of the road surface, providing a reference for traffic safety and traffic management. There are two traditional methods for detecting the state of the road surface: one is the road surface state detection vehicle, which obtains the state of the road surface by installing sensors such as infrared and radar on the vehicle. This method requires the vehicle to drive to the road before it can detect the rain and snow o...

AI Technical Summary

Problems solved by technology

This method requires the vehicle to drive to the road before it can detect the rain and snow on the road surface. It is difficult to adapt to the complex and changeable traffic conditions and the ever-changing weather, and there are great limitations in time and space.

The second is a multi-sensor-based road surface state detection system. This technology requires pre-embedded sensors on the roadside or under the road. The one-time infrastructure investment required is relatively large, and the interval between detection stations is relatively large, about 10km. It can only detect the road conditions in the point-shaped limited area of ​​the road section, and cannot fully reflect the overall situation of the entire road section. The amount of monitoring information is small and it is not suitable for expansion

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