Unsupervised Infrared Monocular Visual Ranging Based On Homometric Estimation

Monocular depth estimation is currently a hot research direction,especially in the field of automatic driving,where obtaining accurate depth information plays a decisive role.Monocular depth estimation based on unsupervised learning is a research hotspot in the field of depth estimation,which can quickly obtain the relative depth of all points in the image.At present,this method is mainly trained through visible light data sets,so it is greatly affected by the environment.For example,it is difficult to obtain accurate depth estimation at night or in bad weather such as sand,dust,and rainstorms,and the practicability is greatly reduced..Therefore,this thesis aims at this problem and studies how to apply the monocular depth estimation algorithm based on unsupervised learning to infrared images that are relatively less affected by illumination,and improve the algorithm according to the characteristics of infrared images and practical application requirements,so that the model It can be applied to driving at night or in bad weather with low light to improve driving safety.This thesis aims to solve the problem that it is difficult to accurately obtain the real distance between pedestrians and vehicle targets during driving at night or in bad weather.The main work done includes:1.Production of infrared data sets.The HD1280 monocular infrared camera developed by Yantai Iray Optoelectronics Technology Co.,Ltd.was used to shoot 28,000 infrared data sets with a resolution of 1280×1024.This data set is mainly collected on roads with many outdoor vehicles and pedestrians.This data set contains a large number of scenes such as roads,vehicles,pedestrian targets,trees and houses,and is suitable for autonomous driving research.2.Apply the monocular unsupervised depth estimation method to the infrared image to obtain the relative depth of the infrared image.Since most of the existing monocular unsupervised depth estimation algorithms are designed based on visible light images,in view of the above situation,it is necessary to improve the unsupervised monocular depth estimation algorithm to make it suitable for infrared images.We propose to use the homography estimation network to improve The pose estimation network in the original model makes the monocular depth estimation results more accurate;since the data set used for training cannot contain dynamic scenes,we propose an automatic masking mask to shield the dynamic scenes in the image.3.An infrared image absolute depth estimation algorithm is proposed,which can be used to obtain the distance of pedestrian and vehicle targets relative to the camera in infrared images.Combined with the YOLOv5 target detection algorithm,pedestrian and vehicle targets in infrared images can be obtained,and the scale factor between relative depth and absolute depth can be calculated through the algorithm,and the relative depth of pedestrian and vehicle targets obtained in the depth estimation model can be converted into absolute depth.By training the algorithm model on the self-constructed infrared image dataset,the experimental results show that the average relative error of the method in the range of 20 meters is 7.32%,and the relative error can be basically controlled below 7% within 10 meters,and the relative error after more than 10 meters is large,which can achieve more accurate ranging in short distances,and the inference speed of a single image can also meet the real-time requirements of engineering.