Design And System Implementation Of On-line Inspect Model For Surface Defects Based On Machine Vision

In recent years,with the appearance of concepts such as the Industrial Internet and"Made in China 2025",the transformation of industrial production informatization is in full swing.The traditional manual visual inspection method cannot detect defective products at high speed and stability,while the detection method based on machine vision can solve this problem well.Machine vision technology is widely used for defect detection in the fields of electronic components,printing and food packaging,the inspection equipment has not formed a standardized general architecture.Existing inspection equipment has shortcomings in speed,anti-interference and scalability,so it is of great research significance and practical value to design a general system model based on machine vision technology for high-speed surface defect detection.The main research results of this article are as follows:1.Design a FlowGD model of surface defect detection system driving by data flowIn order to improve the versatility of surface defect inspection equipment,this paper designed a FlowGD surface defect inspection system model driving by data flow was designed.This model is divided into three sub-modules:a universal tracking controller,a universal data processing center,and a universal human-computer interaction.The various sub-modules are connected through the information flow to intercept the data required in the data flow and upload the collected data,which is suitable for the surface defect detection of two-dimensional images.2.Propose various sub-module hardware decoupling methodsIn order to further improve the versatility of the model,designed a hardware decoupling method for each sub-module.The tracking controller module designed an object-oriented high-speed tracking control method,using the middle-level interpreter to implement the tracking control logic,and can be easily transplanted on various hardware platforms.The data processing center module has designed a detection algorithm chain structure,without changing the originally designed algorithm,only the algorithm unit needs to implement the underlying adaptability for different architecture hardware.The human-computer interaction module has designed a distributed monitoring method based on TCP/IP protocol.The detection program and the monitoring program run separately,so that the monitoring program can run on various platforms.Finally,in order to enhance the system's multi-target defect recognition capability,a multi-target defect detection algorithm based on deep learning is integrated and modified.3.Realized inspection system and proposed verified methodsIn order to verify the practicability of the designed models and methods,based on the FlowGD model,this paper designed a capacitance inspection system using traditional visual algorithms and a SMT inspection system using deep learning algorithms,respectively implemented on industrial PCs and embedded AI platforms.The capacitance inspection system can work stably at a speed of 80,000/hour.The SMT inspection system can accurately identify multiple missing components at the same time at a speed of 3600/hour.After analyzing and comparing the experimental results,the FlowGD model and methods designed in this paper have a stronger generality and applicability,are suitable for the detection of surface defects in electronic components,food packaging and glass.