Research On Automatic Detection Method Of The Inner Bore Surface Of Artillery Tube

As a conventional weapon of modern warfare,artillery has a very important effect on the battlefield situation.Rifling is a key structure on the inner wall of the artillery barrel,which can make the projectile rotate to improve the range,accuracy and power.The rifling will be worn during the use of the artillery,and the degree of wear basically determines the service life of the artillery.In the production process of the artillery,the rifling size is also an important criterion for judging whether the artillery barrel is qualified.Therefore,how to achieve accurate,rapid,and automated measurement of artillery rifling is of vital importance for improving the production efficiency of artillery,ensuring shooting accuracy,and accurately predicting the remaining service life.At present,domestic rifling size testing is mostly conducted by mechanical contact measurement methods such as gauges,plug gauges,and inner diameter dial gauges.For defect detection,spy borers are often used to judge defects.This process is in terms of measurement speed,repeatability,and accuracy.There are great limitations in such aspects.This paper proposes a detection method based on the combination of laser and vision for rifling detection.This method uses a laser displacement sensor to quantitatively detect the rifling diameter of the artillery.After obtaining the displacement data of the rifling section of the artillery,the rifling RANSAC algorithm is proposed to eliminate the difference between the yin and yang line of the artillery and the surface defects of the inner bore,and then the obtained yin line,Yang line data is fitted and optimized,and the diameter of the rifle is calculated.In order to detect rust spots and other defects in the bore,the HOG+SVM rifling defect detection algorithm is proposed to identify them.In order to verify the proposed method,a crawling robot detection system suitable for 155 mm caliber artillery was built,and the rifling data was collected by the crawling robot carrying various sensors,and finally the algorithm was verified on the computer processing platform.Through experimental verification,this method realizes the automatic detection of rifling size and defects,and the experimental results verify the effectiveness of this method.