Jan 4, 2021 · In this article, we propose an efficient convolutional neural network for defect segmentation and detection.

The proposed convolutional neural network for fabric defect detection significantly outperforms eight state-of-the-art methods in terms of accuracy and ...

Jan 15, 2021 · The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms ...

This paper proposes an improved YOLOv4 algorithm with higher accuracy for fabric defect detection, in which a new SPP structure that uses SoftPool instead of ...

Firstly, the fabric dataset without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as ...

Jul 5, 2023 · In order to meet the stringent requirements of textile defect detection, we propose a novel AC-YOLOv5-based textile defect detection method.

At present, most of the defect detection methods employ an offline method, which means that the fabric defects are detected after it is taken off the loom.

The proposed deep learning technique on LSTM infers the details about the fabric through digital images. The defects in fabric are identified using LSTM method.

The paper [2] proposed a new deep convolutional neural network (PTIP), which used local images in the training stage and whole fabric images in the testing ...

Feb 17, 2024 · At present, fabric defect detection algorithms based on deep learning have achieved good results, but there are still some key problems to be ...