Highway severe weather identification method based on artificial intelligence

Abstract

The invention discloses a highway severe weather identification method based on artificial intelligence. The method comprises the following steps: S1, forming video data; s2, classifying the video data according to weather states, dividing the video data into five categories of sunny days, cloudy days, rainy days, foggy days and snowy days, and storing the five categories respectively; s3, forming extracted pictures, and storing corresponding categories; s4, forming an analysis model; s5, forming a key point pixel; s6, forming a comparison database; s7, monitoring the highway environment in real time through a camera, setting a comparison period, and comparing and analyzing the video state and the comparison database; s8, outputting a comparison result after the weather types accord with the weather types of one category; and S9, the type of the identified severe weather is determined, early warning prompt is performed, and final judgment is performed by combining an identification result with temperature and humidity changes, so that interference caused by misjudgment is avoided, high-efficiency identification and early warning are facilitated, the safety of highway management and use is ensured, and popularization and use are facilitated.

Description

technical field [0001] The invention relates to the technical field of expressway management, in particular to an artificial intelligence-based identification method for severe weather on expressways. Background technique [0002] In today's social life, the use of expressways is becoming more and more common, and in the driving safety of expressways, the influence of weather is the largest and the consequences are the most serious. Therefore, in order to ensure the safety of expressway management, it is necessary to To identify severe weather and ensure the timeliness of early warning. [0003] However, in the existing bad weather recognition methods for highways, most of them directly take pictures through surveillance cameras, and then carry out manual recognition and verification. It requires a lot of labor and time, and the cost of recognition is high, and it is impossible to carry out intelligent recognition and analysis through images. A new recognition method needs ...

AI Technical Summary

Problems solved by technology

[0003] However, in the existing bad weather recognition methods for highways, most of them directly take pictures through surveillance cameras, and then carry out manual recognition and verification. It requires a lot of labor and time, and the cost of recognition is high. It cannot be intelligently recognized and analyzed through images. A new recognition method needs to be proposed.