Research On Training Bullet Detection System Based On Machine Vision

Automatic detection technology plays an important role in industrial production and social development.Among them,the industrial detection technology based on machine vision has become the main research direction of automatic detection technology with its advantages of non-contact,good stability and fast detection speed.However,the detection accuracy still needs to be improved,and the detection targets need to be more diversified.Training bullet is a kind of ammunition for military exercises and training.Because of its potential safety hazards,the size requirement is very strict.At present,the detection of training bullet is still a "manual + measuring instrument" method,because of its low detection efficiency,poor reliability and other shortcomings,so it needs to be improved urgently.In this paper,the overall scheme of the training bullet detection system is proposed,and the machine vision detection algorithm in the system is studied and optimized to realize the training bullet detection system.The main research contents are as follows:1.Design the detection system of training bullet.Firstly,the hardware selection rules are analyzed,and the hardware of the training bullet detection system is determined according to the detection requirements of the training bullet.Then,the software arithmetic flow of the training bullet detection system is designed,and the processing modules are analyzed.Finally,the system calibration algorithm is studied,the principle of system calibration is analyzed and its implementation process is summarized.2.The core algorithm of training bullet detection system is studied.Firstly,the principle and implementation process of filtering algorithm in image preprocessing are analyzed,and filtering algorithm of the system is determined by experiments.According to the characteristics of training bullet image,ROI region extraction algorithm is designed.Then,the edge detection algorithm is studied,the principle and implementation process of traditional edge detection algorithm are analyzed,and its advantages and disadvantages are summarized through experiments.The subpixel edge detection algorithm based on Zernike moments is studied,and the error factors are analyzed.The method of dual positioning combined with parity template is used to optimize the algorithm.Finally,the linear fitting algorithm is studied.Aiming at the poor fitting effect of the traditional least squares fitting algorithm when there are many outliers,Tukey weight function is used to optimize the traditional least squares fitting algorithm,and the experimental verification is carried out.3.The test and analysis of the training bullet detection system.Firstly,the software platform of the training bullet detection system is selected and the software algorithm of the system is tested.Then,the training bullet detection system is calibrated,and the geometric relationship between image coordinate system and world coordinate system is constructed.Finally,the test error and stability of the training bullet detection system are analyzed and summarized.