基于MFA-UNet的铜制螺纹零件外表面缺陷检测OA北大核心CSTPCD

In industrial settings,detecting surface defects on copper threaded parts often faces challenges of low efficiency and poor accuracy.To address this,it proposes a copper threaded part surface defect detection algorithm based on MFA-UNet(Multi-Scale Features and Attention Fused UNet).Firstly,a dual down sampling module is designed,utilizing both ordinary convolution and dilated convolution to enhance the model's feature extraction capabilities.Secondly,a compound spatial attention module is integrated into the skip-connection part to improve the model's ability to extract spatial and edge information.Subsequently,a squeeze and excitation module is incorporated during the upsampling process to enhance the model's expressive power and feature selection ability.Lastly,it proposes a similarity comparison algorithm that measures the similarity between segmented images and mask images to obtain the defect detection results.Experimental results demonstrate that the proposed segmentation model a-chieves a PA of 94.81％and an MIoU of 93.78％on the copper threaded part defect detection dataset.The defect detection ac-curacy of the proposed algorithm reaches 98.9％,meeting the requirements for industrial field applications.