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• W3035102456 startingPage "9681" @default.
• W3035102456 abstract "Visual surface defect inspection for metal part has become a rapidly developing research field within the last decade. But due to the variances of defect shapes and scales, the inspection of tiny and irregular shape defects has posed challenges on the robustness of the inspection model. In this context, a deep learning method based on the deformable convolution and concatenate feature pyramid (CFP) neural networks is proposed to improve the inspection. We design a deformable convolution layer in the neural networks as an attention mechanism to adaptively extract the features of defect shape and location, which enhances the inspection of the defects with large shape variances. We also merge the multiple hierarchical features collected from different deformable convolution layers by the CFP, which improves the inspection of tiny defects. The results show that the proposed method has a better generalization ability than traditional convolution neural networks." @default.
• W3035102456 created "2020-06-19" @default.
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• W3035102456 date "2020-12-01" @default.
• W3035102456 modified "2023-10-07" @default.
• W3035102456 title "Visual Defect Inspection of Metal Part Surface via Deformable Convolution and Concatenate Feature Pyramid Neural Networks" @default.
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• W3035102456 doi "https://doi.org/10.1109/tim.2020.3001695" @default.
• W3035102456 hasPublicationYear "2020" @default.
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