Copper threaded part surface defect detection algorithm based on MFA-UNet

doi:10.16731/j.cnki.1671-3133.2024.05.015

Abstract

Abstract: 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 achieves a PA of 94.81 % and an MIoU of 93.78 % on the copper threaded part defect detection dataset. The defect detection accuracy of the proposed algorithm reaches 98.9 %, meeting the requirements for industrial field applications.

Key words: part defect detection, image segmentation, attention mechanism, similarity comparison

CLC Number:

TP391

MA Tao, LI Jingzhao. Copper threaded part surface defect detection algorithm based on MFA-UNet[J]. Modern Manufacturing Engineering, 2024, 524(5): 113-120.

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