Feature Point Detection and Description Based on an Improved KeyPointNet Network
Traditional hand-designed feature extraction methods such as SIFT and ORB are not as robust and accurate as deep learning-based feature point detection networks for feature extraction in challenging scenarios such as lighting or viewpoint changes.Inspired by the robustness of KeyPointNet network performance in image feature extraction tasks,this paper uses lightweight networks to design an improved model of KeyPointNet,aiming to make it satisfy a certain level of accuracy and run in real-time on resource-constrained platforms.The experimental results show that the improved KeyPointNet outperforms the original KeyPointNet model in terms of repeatability and single response accuracy on the HPatches dataset,and the improved network model compresses the number of parameters by approximately 88.83％and reduces the number of floating-point operations by about 86.62％,which is more suitable to be deployed in real-world scenarios.
deep learninglightweight networksimage feature extractionKeyPointNet
万方数据
维普
