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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Fabric defect detection using deep learning: An Improved Faster R-approach
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Fabric defect detection using deep learning: An Improved Faster R-approach

机译：使用深度学习的织物缺陷检测：提高了更快的R-方法

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摘要

In view of the various types of fabric defects, complex textures and the lack of existing defect detection technology, this paper proposes a new fabric defect detection technology based on deep learning. Specifically, based on the Faster R-CNN network model, a deep residual network is used instead of the traditional VGG-16 for feature extraction. In order to increase detailed shallow features, a feature pyramid model of different scales is constructed as the input of the RPN network. And the number of anchors are increased to adapt to small object detection scenarios. In addition, the regularized softmax classifier is used for training to improve the network convergence ability and classification accuracy. Experimental results on fabric defect dataset show that the improved model has fast convergence speed and excellent model performance. The average precision on the defect dataset has reached 94.66%, which is 4.35% higher than the original model.
机译：鉴于各种类型的织物缺陷，复杂纹理和缺乏现有缺陷检测技术，本文提出了一种基于深度学习的新型织物缺陷检测技术。具体地，基于更快的R-CNN网络模型，使用深度剩余网络代替传统的VGG-16进行特征提取。为了提高详细的浅功能，构造了不同尺度的特征金字塔模型作为RPN网络的输入。增加锚点以适应小对象检测方案。此外，正则化软MAX分类器用于培训以提高网络收敛能力和分类准确性。织物缺陷数据集的实验结果表明，改进的模型具有快速收敛速度和优异的型号性能。缺陷数据集的平均精度已达到94.66％，比原始模型高4.35％。

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