Performance Optimization For Generalized Hough Transform Based On GPU And The Application On Hadoop Platform

With the development of computer technology.Computer Vision Technology continues to be mature.It has become increasingly widespread in the industrial field.As a hot issue in the field of Computer Vision.Object detection technology has gained extensive attention.Generalized Hough Transform(GHT)is an important object detection algorithm.But because of the complexity of the algorithm’s space and time complexity,it cannot be well suitable for real-time applications.Therefore,it has urgent need to improve the performance of the algorithm in practical applications.This thesis introduced the theory of the Generalized Hough Transform algorithm.Aiming at high time complexity of Generalized Hough Transform,the use of GPU is parallel to optimize its performance.by analyzing the potential parallelism in the each step of the algorithm.With the rapid development of social science and technology,the ability of data acquisition is enhancing,and the demand for large data processing is growing quickly.As a parallel data processing framework，Hadoop apply to distributed data processed more widely.In this paper,the GPU optimization algorithm of Generalized Hough Transform is applied to Hadoop platform,so it can handle object detection of massive picture in practical applications.It provides a solution for the rapid detection of the target object in big data environment.Experiments illustrate that when dealing with a large number of images detection.GPU optimization algorithm combined with Hadoop platform can get good speedup.Contributions of this thesis are as follow ing:1.The Analysis about the theory of Generalized Hough Transform and the existing research in the field of object detection are presented.Based on the original single object detection,the ability of multiple similar objects detection and counting is added.According to the threshold setting,many objects are identified from the image quickly and accurately,and the related position inf-ormation of these objects is recorded.2.Generalized Hough Transform algorithm is accelerated on GPU by exploiting the potential parallelism in the various steps of the algorithm and reasonably arranging the stored data in each memory.Combined GPU memory optimization methods,the use of CUDA language programming implement GPU optimization of the algorithm.Experiments illustrate that,GPU greatly enhances the running speed of the algorithm.3.Aiming at complex problems faced by object detection of massive pictures in big data environment,the optimization algorithm of Generalized Hough Transform is applied to Hadoop platform.The "GPU+Hadoop" architecture,using Hadoop technology combined with CUDA programming framework,optimize the realization of object detection of massive pictures and improve the efficiency of multi-map multi-target detection.