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| Fabric Defect Detection Based on Deformable Dense Convolutional Neural Network |
| ZHUANG Ji-chao,GUO Bao-su,WU Feng-he |
| School of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract A deformable dense convolutional neural network is proposed for the traditional fabric defect detection method which cannot be applied to defect features with large-scale changes and small area ratios. To pay more attention to the feature information that is far away in the image and avoid capturing the texture information, deformable convolution is employed to enhance the semantic expression of the feature. By setting the respective direction x and y offsets of the convolution pixels relative to the center pixel in the convolution layer, and using backpropagation training offsets to increase the deformation adaptability of the receptive field. Meanwhile, a dense connection manner is utilized to keep the model from missing edge defect information. Finally, the classification and location detection of defects is realized based on the defect prediction and the border regression. Experimental results show that the average accuracy of the proposed approach and the standard deviation of single-type target detection accuracy is 93.53% and 2.5139, respectively, compared with other methods, it is more competitive.
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Received: 01 September 2021
Published: 21 February 2023
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2023, Vol. 44 
