Научная статья на тему '基于深度学习的织物疵点检测方法'

基于深度学习的织物疵点检测方法 Текст научной статьи по специальности «Компьютерные и информационные науки»

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Ключевые слова
疵点检测 / FASTER RCNN / 残差结构

Аннотация научной статьи по компьютерным и информационным наукам, автор научной работы — 石屹山

织物的疵点检测是织物质量把控的关键一步, 也是决定产品质量的关键所在, 在目前的织物疵点检测系统中, 识别精度低, 算法泛化水平差, 系统鲁棒性不足导致织物小目标疵点检测遗漏的缺陷. 针对上述问题, 本文基于 Faster RCNN的织物疵点检测算法, 使用何凯明提出的 Deep Residul Net 为特征提取网络, 选取 ResNet50 的残差结构, 扩充候选框使其与 FPN 融合, 通过 ROI Align 更精确的定位织物疵点, 再引入 CBAM (convolutional block attention module) 卷积模块注意力机制, 更进一步优化网络提取特征, 提高织物疵点检测的准确率.

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METHOD ON FABRIC DEFECT DETECTION BASED ON DEEP LEARNING

Fabric defect detection is a key step to control fabric quality and determine product quality. In the current fabric defect detection system, low recognition accuracy, poor algorithm generalization level and insufficient system robustness lead to the defects missed in fabric small target defect detection. To solve the above problems, based on the fabric defect detection algorithm of Faster RCNN, this paper used Deep Residul Net proposed by He Kaiming as the feature extraction network, selected the residual structure of ResNet50, expanded the candidate box to fuse it with FPN, and positioned the fabric defect more accurately through ROI Align. Then, the attention mechanism of convolutional block attention module (CBAM) is introduced to further optimize the network feature extraction and improve the accuracy of fabric defect detection.

Текст научной работы на тему «基于深度学习的织物疵点检测方法»

For citation-. Shi Yishan. Method on Fabric defect detection based on deep learning //

URL: http.//rectors.altstu.ru/ru/periodical/archiv/2022/2/articles/2_4.pdf EDN. https://elibrary.ru/moqqbn

UDK 677.019

Method on Fabric defect detection based on deep learning

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[1] Girshick R., Donahue J., Darrell T.et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014:580-587.

[2] Girshick R. Fast r-cc[C] // Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015:1440-1448.

[3] Ren S., He K., Girshick R. et al. Faster r-cnn: towards real-time object detection with region proposal networks [C] // IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6):1137-1149.

[4] Liu Wei, et al. SSD: single shot multibox detector[C] // European Conference on Computer Vision, 2016:21-37.

[5] Redmon Joseph, et al. You only look once: Unified, real-time object detection [C] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016:779-788.

[6] Redmon Joseph, et al. Y0L09000: better, faster, stronger [C] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017:7263-7271.

[7] Redmon, Joseph, et al. Yolov3: an incremental improvement [C] // Computer Vision and Pattern Recognition, arXiv preprint arXiv:1804.02767,2018.

[8] [J]. 2014(03)

References

1. Girshick R., Donahue J., Darrell T., et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014:580-587.

[2] Girshick R. Fast r-cc[C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015:1440-1448.

[3] Ren S., He K., Girshick R., et al. Faster r-cnn: towards real-time object detection with region proposal networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6):1137-1149.

[4] Liu, Wei, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision, 2016:21-37.

[5] Redmon, Joseph, et al. You only look once: Unified, real-time object detection [C] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016:779-788.

[6] Redmon, Joseph, et al. Y0L09000: better, faster, stronger [C] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017:7263-7271.

[7] Redmon, Joseph, et al. Yolov3: an incremental improvement [C] //Computer Vision and Pattern Recognition, arXiv preprint arXiv:1804.02767,2018.

[8] Li Wenyu, Cheng Longdi. Fabric Defect Detection Based on Machine Vision and image Processing [J]. Journal of Textile Science and Technology, 2014(03)

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