Research On Crack Detection Method Of Diamond Saw Blade Based On Deep Learning

Defect detection of diamond saw blade is an important guarantee for product quality and safety,but diamond particle noise makes it more difficult to detect saw blade cracks.For the current common target detection methods,the effect is not very good in order to achieve real-time rapid and accurate detection.This paper will conduct a detailed study on how to efficiently detect diamond cracks.Due to the influence of the material characteristics of diamond saw blade and the problems existing in the processing scheme in the production process,the surface of the saw blade will have corresponding cracks,which will lead to the occurrence of fatigue fracture and other conditions.These problems will have great harm to users.Therefore,it is great practical value to carry out targeted analysis on this topic.Firstly,aiming at the noise problem in the image,this paper carries out effective filtering and denoising processing.Secondly,the effective YOLOv3 algorithm is adopted for crack detection and identification.The algorithm is based on one-stage method and has the advantages of high detection efficiency,which makes the algorithm not only meet the real-time requirements for crack identification in actual production,but also has strong autonomous ability of learning.In the analysis of the network structure,corresponding optimization measures are taken for the initial feature structure,thus forming a pyramid scheme with richer scales and multiple levels of division.Through effective integration between high and low levels,prediction layer data covering four levels of scales can be obtained,which is beneficial to obtain more basic characteristics and position data about target objects.Using the method of deep learning,the neural network will automatically learn and master the deep features in the big data system,thus replacing the basic features constructed manually,which can realize more accurate description of features and further realize a substantial increase in recognition rate.Design the corresponding detection process,preprocess the early target obj ect and improving the structure of neural network,achieve the real-time detection task of the object,and finally it could make the design model show more promin-ent advantages.