Research on Fast R-CNN for Visual Object Detection

作     者：GHULAM ARBI 

作者单位：华南理工大学 

学位级别：硕士

导师姓名：YU ZHULIANG

授予年度：2018年

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

摘      要：Over the last decade,object detection has emerged as an important area of research for computer *** this regard,some novel and impressing CNN architectures have been *** this work Fast R-CNN and VGGNet architectures have been incorporated for the given task of visual object *** proposed methods have been exercised and empirically it is realized that Fast R-CNN architecture exhibits considerable performance for the given *** particular for given problem,emphasis has been rendered on visual object Capra Falconeri（Markhor）,in different challenging environment and background *** most issue for object detection using deep learning techniques is to save the training and testing time without sacrificing its ***,existing Fast R-CNN technique provide good accuracy but it suffers from more training and testing time that limits the overall performance of *** this thesis,a new dataset is created named as Markhor-VI by various data augmentation methods such as translation,rotation,scaling and so *** we have modified existing Fast R-CNN technique by transfer learning by various methods from layer 1 to layer *** is found that convolutional object detection is still evolving as a technology,despite outranking other object detection *** is also found that transfer learning can save a lot of resources if fine tuning is performed in a particular way.