基于深度学习SuperGlue的枪弹痕迹自动识别方法

耿乐; 沐春华; 张浩; 虞浒

基于深度学习SuperGlue的枪弹痕迹自动识别方法

引用本文：耿乐，沐春华，张浩，虞浒.基于深度学习SuperGlue的枪弹痕迹自动识别方法[J].计算技术与自动化,2023,(1):174-178

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作者	单位
耿乐，沐春华，张浩，虞浒	(南京工业大学机械与动力工程学院，江苏南京 211800)

中文摘要:为实现枪弹痕迹自动匹配的准确性，提出了将基于深度学习的SuperPoint特征提取和SuperGlue匹配算法引入枪弹痕迹自动识别研究。通过SuperPoint网络提取弹痕图像特征点位置与描述子向量；研究了SuperGlue的匹配机制，包括注意力机制的图神经网络(GNN)及优化匹配层，将提取的弹底窝痕迹的特征点和描述子使用SuperGlue算法进行匹配。实验表明SuperPoint特征通过SuperGlue匹配，相较于机器学习算法实现了更高的匹配准确度，正确匹配数量提高，为枪支鉴定增加科学性。

中文关键词:枪弹痕迹超级点算法超级胶水算法痕迹匹配深度学习

Automatic Identification Method of Bullet Marks Based on Deep Learning SuperGlue

Abstract:In order to achieve the accuracy of automatic matching of bullet traces, it is proposed to introduce the deep learning-based SuperPoint feature extraction and SuperGlue matching algorithm into the automatic identification of bullet traces. The position and descriptor vector of bullet marks image feature points are extracted through SuperPoint network; the matching mechanism of SuperGlue is studied, including attention graph neural network (GNN) and optimized matching layer, and SuperGlue algorithm is used to extract the feature points and descriptors of bullet bottom pit marks to match. Experiments show that SuperPoint features are matched by SuperGlue, which achieves higher matching accuracy than machine learning algorithms, and increases the number of correct matches, adding scientificity to gun identification.

keywords:bullet marks SuperPoint SuperGlue trace matching deep learning

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