Lightweight method of feature point extraction and matching incorporating a progressive strategy
To address the issues of the feature-matching method based on the SuperPoint network,such as low accuracy in feature-point extraction and high computational cost under challenges of lighting,pose and angles,a lightweight feature point extraction and matching method under a progressive strategy is put forward.Firstly,to reduce the model's computational cost,the SuperPoint network is modified using depthwise separable convolution.Secondly,an attention module is built in the feature extraction part to strengthen the network's spatial feature extraction capability.Also,a progressive multi-scale feature fusion module is designed to capture object details and boost feature representation capabilities.Finally,the obtained feature points are matched using the SuperGlue algorithm.Experimental analysis on the Hpatches dataset shows that the proposed algorithm achieves an average matching accuracy(mAP)of 86%and feature point repeatability(Rep)of 70%in illumination change scenarios,and mAP of 78%and Rep of 68%in viewpoint change scenarios.The proposed algorithm not only shows certain advantages in feature matching,but also achieves good results when applied to video stitching.
feature point extractionfeature point matchinglightweightattention mechanismprogressive multi-scale feature fusion
万方数据
维普
