A Research on Surface Defect Detection of Metal Parts Based on Machine Vision
In the current manufacturing industry,the occurrence of defects in metal parts can result in substantial economic losses.The primary challenge lies in the small size and random distribution of these de-fects.Traditional manual detection struggles to differentiate between defective and non-defective areas due to their subtle nature,leading to high labor costs and low economic benefits.To solve this issues,a method for surface defect detection of metal parts based on machine vision is proposed.Machine vision detection replaces human labor,while an Interactive Spatial Position Attention Module is employed to tackle the difficulty of de-tecting inconspicuous surface defects on metal parts.Additionally,a Dual Local-global Transformer Module is utilized to enhance the distinction between defect areas and surrounding normal areas,thereby improving the detection performance for small surface defects on metal parts and ultimately enhancing enterprises'economic benefits.
machine visiondefect detectioninteractive spatial position attention moduledual local-global transformer module
万方数据
维普
