Research On Pavement Crack Image Stitching Technology

Image stitching refers to the combination of multiple images with a certain overlapping area rate acquired by the image acquisition device into one image,which has a full picture of each image.Image stitching technology is an important part in the field of digital image processing.With the increasing requirements for image quality and the limited resolution of ordinary cameras,it is impossible to obtain large angle of view and clear image at the same time.Image stitching technology can get wide scene,high resolution and integrate multiple images information into one stitching image,which satisfys people's needs.Image stitching technology is increasingly appearing in people's daily lives,such as panoramic image capture in smartphones,aerial view of drone aerial photography,Google Map Street View,virtual reality in games,and security.In this paper,the key technologies involved in pavement crack image stitching are intensively studied,including image feature point detection and matching,internal and external camera parameter estimation and optimization,and image fusion.The work done in this paper and the main innovations include:(1)Two main feature detection and description algorithms,SIFT algorithm and ORB algorithm,are studied.In view of the fact that the SIFT algorithm has redundancy when the Gaussian pyramid is established,this paper appropriately reduces the number of groups and maintains the number and stability of feature point detection.Combined with the advantages and disadvantages of the improved SIFT algorithm and ORB algorithm,and the performance of the two algorithms in the detection of pavement cracks,the improved SIFT algorithm is used as the feature point detection algorithm,and rBRIEF algorithm for describing the feature points in the ORB algorithm is used as the feature point description algorithm.The description algorithm is called the BSIFT algorithm.Experiments show that the robustness of BSIFT algorithm is similar to that of SIFT algorithm,which is better than ORB algorithm and faster than SIFT algorithm.(2)Bundle adjustment algorithm is studied and improved.Since the traditional bundle adjustment algorithm needs to put all the matched interior points into the optimizer for numerical optimization,the optimization iteration takes a long time because of many matching points.In this paper,the matching point pairs are sorted based on the confidence,and the number of matching point pairs is set as the threshold value.The influence of multiple threshold values on the stitching speed and the stitching image quality is compared.Finally,the matching point pair is used as the threshold value of 30.(3)Image fusion algorithms are studied,including best seam-line search and image fusion.Aiming at the ghost phenomenon that may occur in overlapping regions,this paper proposes a best seam-line search algorithm based on distance transform.According to the distance criterion,the pixel of the overlapping part of the adjacent image is closer to which image,and the pixel belongs to the corresponding image.And compare the two commonly used best seam-line search algorithms--dynamic programming method and graph cutting method.Experiments show that the best seam-line obtained by this algorithm is similar to the other two algorithms,but the processing speed is obviously better than the other two algorithms.