基于共享联结三元组卷积神经网络的枪弹膛线痕迹快速匹配方法

针对传统通过激光检测提取膛线线形痕迹信号时枪弹痕迹检测精度不高且操作复杂的问题,提出了新型提取和处理方法.采用多尺度配准、弹性形状度量与卷积神经网络技术,基于多模式弹性驱动自适应控制方法,建立了试件末端位置和姿态参数分布模型,采用孤立森林算法检测信号进行异常处理,利用变尺度形态滤波算法去除非细小特征,引入平方速度函数优...

Fast matching method of bullet rifling traces based on sharedconnection triplet convolutional neural network

Aiming at the problems of low precision and complicated operation of traditional bullet trace detection which generally uses laser to detect rifling traces to extract the signal of the rifling traces,new extraction and handling method was provided.By adopting multi-scale registration,elastic shape measurement and convolutional neural network technology,and using multi-mode elastic drive based adaptive control method,the end position and attitude parameter distribution model of the specimen were established.At the same time,the isolated forest algorithm was used to detect the signal for anomaly processing,[JP2]and the variable-scale morphological filtering algorithm was[JP] used to remove non-small features.The square velocity function was introduced to optimize the elastic shape measurement algorithm to complete the curve contour embedding layer mapping.Aiming at the matching part of the rifle line shape,a convolutional neural network model of optimized parameter sharing connection triples suitable for trace features was established,and the network was trained to convergence by calculating the similarity of the embedding layer and minimizing the triple loss function.The comparison of similarity matching experiment results by using different methods was conducted.The results show that the new method solves the accuracy and operability problems faced in the traditional bullet trace detection,the stability of the detection result can be guaranteed,and the cost is greatly reduced compared with the traditional detection method.Adopting multi-mode elastic drive adaptive control method and three-tuple convolutional neural network model in the extraction of rifling traces provides a new feasible method and idea for bullet trace detection.