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.
关键词
特征点提取/特征点匹配/轻量化/注意力机制/渐进式多尺度特征融合
Key words
feature point extraction/feature point matching/lightweight/attention mechanism/progressive multi-scale feature fusion
维普
万方数据