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

Abstract

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.

Keywords

• test and measurement instrument; bullet trace; multi-scale registration; elastic shape metric; triplet loss function; convolution neural network