Research On Vehicle Detection And Distance At Night Based On Monocular Vision

With the year-on-year growth of car ownership,traffic safety issues have received increasing attention.Traffic accidents have caused huge casualties and economic losses.In the night environment,the visibility of the surrounding environment becomes poor,the driver's judgment of road information is not accurate,and the driver has visual fatigue,so the number of traffic accidents in the night environment accounts for the majority.The current intelligent driving assistance systems for automobiles are still incomplete,and they are rarely used at night.If we can identify the vehicle in front at night and get the distance of the vehicle in front,whether it is going straight,turning,or turning around can greatly help the driver make a reasonable judgment and avoid traffic accidents.While researching the night assistance system,it is also necessary to ensure the calculation speed.Based on the above,this paper proposes a monocular vision night vehicle detection and ranging algorithm at night.First,in view of the low-light environment at night,this paper proposes an image enhancement algorithm that improves the operation speed while ensuring the maximum realism of the image.After projection to obtain two types of pictures with lower and higher illuminance,the two types of pictures are image fusion based on principal component analysis.The obtained picture is in a certain area of the two-dimensional plane with normal illuminance.From subjective and objective analysis,compared with the current 12 mainstream algorithms,the algorithm in this paper has certain advantages.Second,for the situation that the outline of the vehicle is not clear at night,this article adopts the method of attention distillation to increase the intensity of vehicle feature extraction.This article uses a two-stage detection method.It is divided into Backbone and Head.In the Backbone section,the attention distillation algorithm in this article treats the process as a student network.The entire network with the attention mechanism assists the training of the teacher network and transfers the teacher's "knowledge" to the student network.Teacher network is only used in training.The aspect ratio of the anchor in the Head is set to {1: 1,4: 3,3: 4,1: 2} according to the shape characteristics of the vehicle,and the attention module HAM is also added to separate the foreground and background.In both parts of the detection unit,a large receptive field of 5 × 5 is used,and the weighted parameters are increased for the mixed channel selection to the channel layer of vehicle characteristics.Third,the depth map in the night environment has few true ground truth,and there are too many occluded and relatively moving objects.This paper proposes an unsupervised network model based on time and scene dimensions to estimate the depth,and combines the unity matrix's.The homography algorithm transforms from relative depth to absolute depth.Finally,this paper is compared with the current 16 monocular depth estimation algorithms.There are four aspects,average relative error,root mean squared error,average log10 error and threshold error.This study has certain reference application value for the development and optimization of intelligent vehicle assisted driving system at night,and plays a positive role in the promotion of intelligent vehicles.