Scratch defect detection model on wooden board surface with complex texture
To improve the automation level of the wood processing production line,a scratch defect detection model on the wood surface based on Faster RCNN was proposed to identify and locate scratch defects under different tex-ture backgrounds.In the image preprocessing stage,an improved bilateral filtering algorithm was proposed to smooth the texture background while maintaining the details of the scratches.A gray-scale adaptive scratch genera-tion method was proposed for data enhancement.The deformable convolution was introduced to enhance the feature extraction ability of the model,and the rotating bounding box was used and a new bounding box regression loss function was proposed to solve the problem that the proportion of scratch defects in the horizontal bounding box was much smaller than the texture background.The images collected by the actual wood board processing production line verified the effectiveness of the proposed model.The proposed model was compared with other defect detection methods,and the results proved the superiority of the proposed model.
scratch detectionFaster RCNNdeformable convolutionrotating bounding boxregression loss
万方数据
维普
