基于Fast RCNN改进的布匹瑕疵识别算法

doi:10.13229/j.cnki.jdxbgxb20190243

Abstract

Abstract:

Fabric defect dataset usually has high resolution but small defect area, which is different from common image classification datasets (such as ImageNet, etc). When applying exist classification algorithms to fabric defect dataset directly, it cannot achieve expected accuracy. To address this problem, this paper proposes a new classification algorithm based on improved Fast RCNN. For an image with small defect area, we follow the pipeline of object detection, generating lots of ROI (Region of Interest), extracting feature map with deep convolutional neural network and predicting each ROI's class probability. On the last stage, we combine all ROIs' class probabilities to get the full image's class probability. Experiments performed in a fabric defect dataset which has 3331 high resolution images show that our algorithm outperforms OurNet and exist classification algorithm.

Key words: computer application, fabric defect recognition, convolutional neural network, image classification, object detection

CLC Number:

TP391.4

Xiang-jiu CHE,Hua-luo LIU,Qing-bin SHAO. Fabric defect recognition algorithm based onimproved Fast RCNN[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2038-2044.

Figures/Tables 12

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	AUC	mAP	Score
OurNet(双网络)	0.787	0.104	0.582
Base?ResNet	0.870	0.198	0.668
本文方法	0.961	0.404	0.794
增加上采样	0.961	0.448	0.807
增加全局信息	0.969	0.476	0.821

Fabric defect recognition algorithm based onimproved Fast RCNN

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