Online Detection System Of Wood Veneer Depth Defect Based On Line Laser Scanning Technology

As a common building material,wood has been widely used in all aspects of people’s daily life.Different from the anisotropic properties of natural wood sheets,plywood has been more and more widely used in recent years due to its consistent characteristics,stable morphology and high utilization rate of materials.As the main manufacturing raw material of plywood,the manufacturing quality of veneer is an important factor affecting the production quality of plywood.At present,most of the veneer manufacturers ’ detection and classification of veneer products are completed by manual visual inspection.This work method has problems that the recognition results cannot be standardized,the detection efficiency is low,and the subjective understanding of workers is greatly affected.At the same time,it is necessary to invest a large number of human resources in the detection process.These drawbacks greatly restrict the development of veneer industry.Continuing to use manual detection methods does not align with current promotion of industrial automation and intelligent trends.Given the above problems,this paper proposes an online detection system for wood board depth defects based on line laser scanning technology and completes the following main research work:Firstly,the depth information of veneer is obtained by line laser scanning technology.The principle of line laser triangulation method is improved to highlight the subtle deep defects on the veneer,and the feasibility of the improved line laser scanning technology is verified by experiments.Based on this,the image acquisition platform of veneer deep defect detection is built,and the acquisition of line laser image samples on the veneer surface is completed.Secondly,aiming at the problem that the depth defects of the veneer surface need high-precision detection and the pixel-level edge detection effect is not good,this paper proposes an improved line laser sub-pixel edge detection algorithm.Using the Canny edge detection algorithm as a coarse positioning algorithm improves the speed of the Zernike moment sub-pixel edge detection algorithm.The Otsu algorithm obtains the more accurate gray judgment threshold,the detection accuracy of the Zernike moment sub-pixel algorithm is improved,and the high-precision extraction of the line laser edge information is completed.The average detection speed of the improved sub-pixel edge detection algorithm is improved by 1.91 s,and the detection accuracy reaches 0.001 pixels,which meets the needs of high-precision detection of the depth defects on the veneer surface.Thirdly,for the problem of edge data classification,firstly,according to the characteristics of experimental data and machine learning,one-dimensional signal processing is performed on the experimental data to build a data set of deep defects on the surface of the veneer.The machine learning classification algorithm is used to classify and identify the edge information data,and the veneer depth defect detection algorithm based on the improved random forest algorithm is designed.The experimental results show that the classification accuracy of the veneer depth defect detection algorithm proposed in this paper reaches 98%in four kinds of depth defects,achieving the expected veneer surface depth defect classification goal.Finally,in order to make the veneer depth defect detection system better applied in actual production,the software system of the veneer depth defect online detection system is designed on the PyCharm platform.Through the software system and local area network communication,the various parts of the single board depth defect online detection system are connected in series,and the host computer controls the whole detection system,and the real-time feedback of the detection information is realized.The effectiveness of the board depth defect detection system designed in this paper is verified.