Research On Single-point Laser Detection Feature Adaptive Matching Technology For Tool Linear Traces

In the criminal investigation process,the shear line traces are used as a kind of tool traces and are often found in cable theft cases.Criminals use large-scale cutting tools such as bolt cutters,cable cutters,and tampering tools when stealing communication cables and penetrating the ground.The linear traces of the decapitated surface formed by shearing are the most common traces found on the scene of the crime.They are not easily damaged,difficult to camouflage,have frequent appearances,and have high identification value.This is very important for the case handlers to determine the nature of the case,determine the tools for committing crimes,and then verify the suspects.The main content of this paper is to study how to perform a series of processing on the traces of the broken surface collected by the shear line trace laser detection equipment,and ultimately to match the crime tools efficiently and accurately.Due to the interference of surrounding environment in the signal acquisition process,the accuracy of the device itself and the subjective factors of the operator's operation,a large amount of interference noise is contained in the detected signal,and these noises need to be eliminated before the signal characteristics are matched.In terms of signal noise reduction,a method based on wavelet threshold denoising is mainly used,and the effectiveness of the method is proved by experimental verification.Next,the features of the signal are extracted and compared.In this paper,the Boosting algorithm design idea is adopted.Several weak classifiers are combined to form a strong classifier.The gradient-based distribution,variance variance,threshold sequence and wavelet transform sequences are combined.The four comparison strategies.The parameters are trained based on the gradient descent method,the optimal combination weights of the four comparison strategies are obtained.Finally,the results are grouped and identified,and the matching conclusions are obtained.Finally,the experiment verifies the effectiveness and efficiency of the algorithm.