基于分割掩码的背光源工业图像去毛刺方法OA

Dust particles adhere to the workpiece surface in industrial environments,which leads to a decline in dimensional measurement accuracy.In view of this,the paper proposes a backlight industrial image deburring method based on segmentation masks.To enhance the ability to capture image detail information and restore overall structure while avoiding the excessive smoothing caused by traditional morphological methods,a global-local feature extraction module(GLFEM)is designed as the core of the feature fusion module(FFM).Selective attention partial convolution(SAPC)and integrated statistical attention(ISA)mechanisms are used to capture key feature information to reduce model complexity and enhance feature representation.A mask adaptive enhancement module and an improved loss function are introduced to further enhance the removal of burrs at contour edges.Experimental results show that in the five measurement indicators for threads,the average errors for major diameter,medium diameter,minor diameter,pitch,and thread angle are 0.000 26 mm,0.004 92 mm,0.005 96 mm,0.000 11 mm,and 0.073°,respectively.In comparison with the existing deep learning methods,the proposed method demonstrates significant advantages in dimensional measurement accuracy.Moreover,the proposed method achieves not only the precise dimensional measurement,but also a balance between real-time performance and accuracy,with parameter count and computational complexity comparable to existing models,making it suitable for deployment in resource-constrained industrial environments.