Design And Implementation Of Pedestrian Detection Method In Rainy And Snowy Weather

In recent years,remarkable advancements have been made in artificial intelligence technology,particularly the detection algorithms of computer vision.As a critical component of this field,pedestrian detection has been remarkably successful.With the proliferation of monitoring equipment,computer vision-based intelligent monitoring systems have become popular in our daily lives,resulting in much efficiency improvement and labor-saving.However,other difficulties will be encountered when the intelligent monitoring system is applied.For example,under complex weather conditions,the pedestrian detection results will become inaccurate due to the loss of visual information and the clutter of the background.Therefore,this article will innovatively propose how to research and implement accurate and efficient pedestrian detection methods under rainy and snowy weather conditions,and based on this,provide practical deployment plans and prototype system design and implementation.This article first conducted in-depth research on pedestrian detection algorithms,roughly dividing existing research into three categories:traditional feature,machine learning,and deep learning methods.The advantages and disadvantages of each type of method were summarized,and the current research status of image enhancement and intelligent monitoring systems involved in this article was also investigated and analyzed.Then,this article focuses on the problem of image quality degradation caused by rain and snow particle occlusion in pedestrian detection during rainy and snowy weather.Considering the real-time requirements of landing applications,a chain image filter enhancement method is adopted,and adaptive learning of filter-related parameters is adopted to enhance and preprocess the images of rainy and snowy weather.In addition,to address the issue of inconsistent learning and optimization of classification and localization branches in pedestrian detection,based on extensive experiments,a final pedestrian detection algorithm model was proposed for pedestrian single classification tasks,and a series of comparative and ablation experiments were designed to verify the necessity of each method module proposed in this paper.Finally,this paper designs and implements a pedestrian detection and analysis prototype system in rainy and snowy weather.This paper envisages an application scenario that detects and monitors pedestrians outdoors and gives real-time warnings of congestion.In this scenario,an in-depth system requirement analysis will be performed to determine the primary functional components and performance requirements of the system.Afterwards,an outline of the system design will be established.The system is divided into data acquisition,data persistence,pedestrian detection analysis,and visualization modules.According to the functional requirements mentioned in the demand analysis,each function’s design and implementation details are given respectively.After the deployment of the whole system,it is ensured that it can be effectively used in the actual situation through multiple tests to meet the set expectations.