Surface defect detection technology of wood-based panel based on image segmentation and deep learning
[Objective]Aiming at the problems of low detection efficiency,low accuracy and digital storage of detection results in the manual detection of surface defects of panel furniture parts,a surface defect detection method of veneer wood-based panel based on image segmentation and deep learning algorithm was proposed.[Method]The defect data set was constructed by the artificial panel images collected by industrial cameras.The global threshold and local dynamic threshold algorithms were used to segment surface defects and image interceptions.The ReLU6 nonlinear activation function was replaced by ReLU function,and the method of reciprocal residual structure was introduced to optimize the MobileNetv 2 deep learning network,and the defect identification and classification were carried out.[Result]The accuracy of the algorithm for the detection of edge breakage and scratch defects on the surface of the veneer panel is 93.1％and 97.5％,and the recall rate is 95.3％and 97.6％,respectively.The average detection time of a single sheet is 163 ms.[Conclusion]The method has high precision and stability,which can solve the problems of low accuracy and low efficiency of traditional manual detection methods,and provide a new idea for automatic detection of surface defects of furniture panels.[Ch,6 fig.3 tab.21 ref.]
万方数据
维普
